Automatically generating nodes and edges in an integrated social graph

ABSTRACT

In one embodiment, a method includes maintaining a data store of nodes and edges and for each of one or more users: scanning items of content associated with the corresponding user node; identifying a candidate item of content; searching for matches between the candidate item of content and existing nodes; determining whether or not a match between the candidate item of content and an existing node exists; and when it is determined that at least one match exists, generating an edge from the user node to the existing node for which the best match is determined; and when it is determined that no match exists, generating a new node based on the candidate item of content, and generating an edge from the user node to the new node.

TECHNICAL FIELD

The present disclosure relates generally to social networking, and moreparticularly, to an integrated social network environment and socialgraph mapped based on the social network environment that includes nodesrepresenting users and concepts in the social network environment aswell as edges that define or represent connections between such nodes.The present disclosure additionally relates to processes forautomatically generating nodes and edges in the social graph. Thepresent disclosure further relates to processes for utilizinginformation extracted from the social graph to dynamically determinerecommendations, such as recommended web pages corresponding torecommended nodes, for display to a user of the social networkenvironment as the user is viewing a structured document.

BACKGROUND

Computer users are able to access and share vast amounts of informationthrough various local and wide area computer networks includingproprietary networks as well as public networks such as the Internet.Typically, a web browser installed on a user's computing devicefacilitates access to and interaction with information located atvarious network servers identified by, for example, associated uniformresource locators (URLs). Conventional approaches to enable sharing ofuser-generated content include various information sharing technologiesor platforms such as social networking websites. Such websites mayinclude, be linked with, or provide a platform for applications enablingusers to view “profile” pages created or customized by other users wherevisibility and interaction with such profiles by other users is governedby some characteristic set of rules. By way of example, a user profilemay include such user-declared information as contact information,background information, job/career information, as well as interests.

A traditional social network is a social structure made of individuals,groups, entities, or organizations generally referred to as “nodes,”which are tied (connected) by one or more specific types ofinterdependency. Social network (graph) analysis views socialrelationships in terms of network theory consisting of nodes and edges.Nodes are the individual actors within the networks, and edges are therelationships between the actors. The resulting graph-based structuresare often very complex. There can be many kinds of edges between nodes.In its simplest form, a social network, or social graph, is a map of allof the relevant edges between all the nodes being studied.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example computer network environment of an examplesocial network environment.

FIG. 2A illustrates example components of an example social networkenvironment.

FIG. 2B illustrates an example architecture of the example socialnetwork environment of FIG. 2A and an example architecture of an exampleclient device of FIG. 1.

FIG. 3 illustrates an example social graph.

FIGS. 4A-4D each illustrates an example user profile page.

FIG. 5 illustrates an example concept profile page.

FIG. 6 shows a flowchart illustrating an example method forautomatically generating nodes and edges based on information currentlybeing entered by a user of a social network environment.

FIG. 7 shows a flowchart illustrating an example method forautomatically generating nodes and edges based on information previouslyentered by users of a social network environment.

FIG. 8 shows a flowchart illustrating an example method for validating aconcept node.

FIG. 9 shows a flowchart illustrating an example method for generatingone or more recommendations for display to a user based at least in parton information extracted from a social graph.

FIG. 10 illustrates an example computer system architecture.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Particular embodiments relate to a social network environment thatincludes an infrastructure or platform (hereinafter infrastructure andplatform may be used interchangeably) enabling an integrated socialnetwork environment. In the present disclosure, the social networkenvironment may be described in terms of a social graph including socialgraph information. In particular embodiments, one or more computingsystems of the social network environment implementing the socialnetwork environment include, store, or have access to a data structurethat includes social graph information for use in implementing thesocial network environment described herein. In particular embodiments,the social graph information includes a first set of user nodes thateach correspond to a respective user, and a second set of concept nodesthat each correspond to a respective concept. As used herein, a “user”may be an individual (human user), an entity (e.g., an enterprise,business, or third party application), or a group (e.g., of individualsor entities) that interacts or communicates with or over such a socialnetwork environment. As used herein, a “concept” may refer to virtuallyanything that a user may declare or otherwise demonstrate an interestin, a like towards, or a relationship with, such as, by way of example,a sport, a sports team, a genre of music, a musical composer, a hobby, abusiness (enterprise), an entity, a group, a third party application, acelebrity, a person who is not a registered user, etc. In particularembodiments, each node has, represents, or is represented by, acorresponding web page (“profile page”) hosted or accessible in thesocial network environment. By way of example, a user node may have acorresponding user profile page in which the corresponding user can addcontent, make declarations, and otherwise express him or herself, whilea concept node may have a corresponding concept profile page (“hub”) inwhich a plurality of users can add content, make declarations, andexpress themselves, particularly in relation to the concept. Inparticular embodiments, the social graph information further includes aplurality of edges that each define or represent a connection between acorresponding pair of nodes in the social graph.

In some embodiments, each edge may be one of a plurality of edge typesbased at least in part on the types of nodes that the edge connects inthe social graph. By way of example, in one particular embodiment, eachedge from a first edge type defines a connection between a pair of usernodes from the first set, while each edge from a second edge typedefines a connection between a user node from the first set and aconcept node from the second set. Furthermore, each edge from a thirdedge type may define a connection between a pair of concept nodes fromthe second set. In such embodiments, the edge itself may store, or bestored with, data that defines a type of connection between the pair ofnodes the edge connects, such as, for example, data describing the typesof the nodes the edge connects (e.g., user or concept), accessprivileges of an administrator of one of the pair of nodes connected bythe edge with respect to the other node the edge connects to (e.g., reador write access of an administrator of one node with respect to theother node connected by the edge), or data describing how or why theedge was first initialized or created (e.g., in response to an explicituser action or declaration, or automatically without an explicit useraction), the strength of the connection as determined by various factorsor criteria related to or shared by the nodes connected by the edge,among other suitable or relevant data. In an alternate embodiment, eachedge may simply define or represent a connection between nodesregardless of the types of nodes the edge connects; that is, the edgeitself may store, or be stored with, identifiers of the nodes the edgeconnects but may not store, or be stored with, data that describes atype of connection between the pair of nodes the edge connects.Furthermore, in any of these or other embodiments, data that mayindicate the type of connection or relationship between nodes connectedby an edge may be stored with the nodes themselves.

Particular embodiments further relate to a method for automaticallygenerating nodes and edges based on information currently being enteredby a user of a social network environment. In particular embodiments,one or more client-side and/or backend (server-side) processes implementand utilize a “typeahead” feature to automatically attempt to matchconcepts corresponding to respective existing nodes to informationcurrently being entered by a user in an input form rendered inconjunction with a requested web page, such as a user profile page,which may be hosted or accessible in, by the social network environment.In particular embodiments, when a match is found, these or otherprocesses may then automatically generate an edge from a nodecorresponding to the user (the user's node) to the existing nodecorresponding to the concept match. Particular embodiments furtherrelate to one or more processes that automatically create a new node andan edge from the new node to the user's node when a match to an existingconcept and corresponding node is not found, or at least not found witha desired level of certainty. By way of example, as will be describedbelow, various web pages hosted or accessible in, the social networkenvironment such as, for example, user profile pages, enable users toadd content, declare interests, or otherwise express themselves(hereinafter also referred to collectively as “declarations”), includingby linking to, or otherwise referencing additional content, such asmedia content (e.g., photos, videos, music, text, etc.), uniformresource locators (URLs), an other nodes, via their respective profilepages or other concept profile pages. Such declarations may then beviewable by the authoring users as well as other users. In particularembodiments, as a user is entering text to make a declaration, thetypeahead feature attempts to match the string of textual charactersbeing entered in the declaration to strings of characters (e.g., names)corresponding to existing concepts (or users) and corresponding concept(or user) nodes in the social graph. In particular embodiments, when amatch is found, the typeahead feature may automatically populate theform with a node name (or other identifier) of the existing node and, asjust described, cause an edge to be created between the matchingexisting node and the user's node. In particular embodiments, as a usercontinues to enter text and the typeahead feature determines that all ora portion of the declaration does not match any existing node, at leastaccording to a statically or dynamically determined level of certainty,the typeahead feature may cause the social network environment toautomatically create a new node based on the declaration entered by theuser, as well as an edge from the user's node to the new node.

Particular embodiments further relate to a method for automaticallygenerating nodes and edges based on information previously entered byusers of a social network environment. In particular embodiments, one ormore backend (server-side) processes implement and utilize a“bootstrapping” feature to automatically attempt to match known conceptsindexed in a data store, each of which may or may not be associated withor correspond to a respective existing node in the social graph, toinformation previously entered by a user in one or more of a variety offorms or formats and stored in the social network environment. Inparticular embodiments, when a match to a known concept is found, theseor other processes may then automatically generate an edge from a nodecorresponding to the user (for which the previously entered informationwas matched) to an existing node corresponding to the concept match.Particular embodiments further relate to one or more processes that,when a match to a known concept is found but where no node currentlyexists for the known concept, automatically create a new node for theknown concept and an edge from the new node to the user's node.Particular embodiments further relate to one or more processes that,when a match to a known concept or existing node is not found, or atleast not found with a desired level of certainty, automatically createa new node based on the previously entered information and an edge fromthe new node to the user's node.

Particular embodiments further relate to a method for populating a“concept database” using data obtained from one or more internal orexternal sources. In particular embodiments, the concept databaseincludes an index of known concepts as well as, in some embodiments,various attributes, metadata, or other information associated with therespective concepts. In particular embodiments, one or more backend(server-side) processes crawl one or more external data sources (e.g.,WIKIPEDIA (www.wikipedia.org), FREEBASE (www.freebase.com, availablefrom METAWEB), or the internet in general) to facilitate or aid ingenerating or populating the concept database. In some embodiments, theconcept database may also be augmented with information extracted fromusers of the social network environment described herein.

Particular embodiments further relate to a method for generating one ormore recommendations for display to a user of a social networkenvironment currently viewing a particular web page or structureddocument hosted at least in part by the social network environment. Inparticular embodiments, one or more server-siderecommendation-generating processes generate the recommendations fordisplay to the user in (on) the currently viewed page based at least inpart on information extracted from a social graph. More particularly,the one or more server-side recommendation-generating processes mayleverage the social graph information including information related tothe user, the currently viewed page, friends of the user who are alsoconnected in some fashion to the currently viewed page, and other webpages or structured documents connected in some fashion to the currentlyviewed page, to determine one or more other web pages or structureddocuments that the user may desire to connect to and then subsequentlygenerate a list or set of these recommended pages for display in somefashion to the user in the currently viewed page.

Various portions of such a social networking platform may be implementedvia a hardware architecture or software framework that enables varioussoftware components or processes to implement particular embodiments, asis described in more detail, by way of example and not by way oflimitation, below. The platform may include one or more hardware orsoftware components, one or more of which may be located or embodied inone or more consolidated or distributed computing systems. Additionally,as used herein, “or” may imply “and” as well as “or;” that is, “or” doesnot necessarily preclude “and,” unless explicitly stated or implicitlyimplied.

As just described, in various example embodiments, one or more describedweb pages or web applications are associated with a social networkenvironment or social networking service. As used herein, a “user” maybe an individual (human user), an entity (e.g., an enterprise, business,or third party application), or a group (e.g., of individuals orentities) that interacts or communicates with or over such a socialnetwork environment. As used herein, a “registered user” refers to auser that has officially registered within the social networkenvironment (Generally, the users and user nodes described herein referto registered users only, although this is not necessarily a requirementin other embodiments; that is, in other embodiments, the users and usernodes described herein may refer to users that have not registered withthe social network environment described herein). In particularembodiments, a registered user has a corresponding “profile” page storedor hosted by the social network environment and viewable by all or aselected subset of other users. Generally, a user has administrativerights to all or a portion of his or her own respective profile page aswell as, potentially, to other pages created by or for the particularuser including, for example, home pages, pages hosting web applications,among other possibilities. As used herein, an “authenticated user”refers to a user who has been authenticated by the social networkenvironment as being the user claimed in a corresponding profile page towhich the user has administrative rights or, alternately, a suitabletrusted representative of the claimed user.

As used herein, a “connection” may represent a defined relationshipbetween users or concepts of the social network environment, which canbe defined logically in a suitable data structure of the social networkenvironment and can be used to define a relationship (hereinafterreferred to as an edge) between the nodes corresponding to the users orconcepts of the social network environment for which the connection hasbeen made. As used herein, a “friendship” represents a connection, suchas a defined social relationship, between a pair of users of the socialnetwork environment. A “friend,” as used herein, may refer to any userof the social network environment with which another user has formed aconnection, friendship, association, or relationship with, causing anedge to be generated between the two users. By way of example, tworegistered users may become friends with one another explicitly such as,for example, by one of the two users selecting the other for friendshipas a result of transmitting, or causing to be transmitted, a friendshiprequest to the other user, who may then accept or deny the request.Alternately, friendships or other connections may be automaticallyestablished. Such a social friendship may be visible to other users,especially those who themselves are friends with one or both of theregistered users. A friend of a registered user may also have increasedaccess privileges to content, especially user-generated or declaredcontent, on the registered user's profile or other page. It should benoted, however, that two users who have a friend connection establishedbetween them in the social graph may not necessarily be friends (in theconventional sense) in real life (outside the social networkingenvironment). For example, in some implementations, a user may be abusiness or other non-human entity, and thus, incapable of being afriend with a human being user in the traditional sense of the word.

As used herein, a “fan” may refer to a user that is a supporter of aparticular web page, web application, or other web content accessible inthe social network environment. In particular embodiments, when a useris a fan of a particular web page (“fans” the particular web page), theuser may be listed on that page as a fan for other registered users orthe public in general to see. Additionally, an avatar or profile pictureof the user may be shown on the page (or in/on any of the pagesdescribed below). As used herein, a “like” may refer to something, suchas, by way of example and not by way of limitation, an interest, a link,a piece of media (e.g., photo, photo album, video, song, etc.) aconcept, an entity, or a page, that a user, and particularly aregistered or authenticated user, has declared or otherwise demonstratedthat he or she likes, is a fan of (as used herein in various exampleembodiments, to “like” or to “fan” something, such as a concept orconcept profile page, may be defined equivalently in the socialnetworking environment and may be used interchangeably; similarly, todeclare oneself a “fan” of something, such as a concept or conceptprofile page, or to declare that oneself “likes” the thing, may bedefined equivalently in the social networking environment and usedinterchangeably herein), supports, enjoys, or otherwise has a positiveview of. As used herein, an “interest” may refer to a user-declaredinterest, such as a user-declared interest presented in the user'sprofile page. As used herein, a “want” may refer to virtually anythingthat a user wants. As described above, a “concept” may refer tovirtually anything that a user may declare or otherwise demonstrate aninterest in, a like towards, or a relationship with, such as, by way ofexample, a sport, a sports team, a genre of music, a musical composer, ahobby, a business (enterprise), an entity, a group, a celebrity, aperson who is not a registered user, or even, in some embodiments,another user (e.g., a non-authenticated user), etc. By way of example,there may be a concept node and concept profile page for “Jerry Rice,”the framed professional football player, created and administered by oneor more of a plurality of users (e.g., other than Jerry Rice), while thesocial graph additionally includes a user node and user profile page forJerry Rice created by and administered by Jerry Rice, himself. Inparticular embodiments, as will be described in more detail below, afriend connection or friendship may define or indicate a logicalconnection defined or represented by an edge between user nodes in thesocial graph, while a like, want, fan, or other connectiondemonstrating, generally, an interest or association may define alogical connection or edge between a user node and a concept node in thesocial graph (and in some embodiments, between two user nodes, orbetween two concept nodes).

Particular embodiments may operate in, or in conjunction with, a widearea network environment, such as the Internet, including multiplenetwork addressable systems. FIG. 1 illustrates an example networkenvironment, in which various example embodiments may operate. Networkcloud 60 generally represents one or more interconnected networks, overwhich various systems and hosts described herein may communicate.Network cloud 60 may include packet-based wide area networks (such asthe Internet), private networks, wireless networks, satellite networks,cellular networks, paging networks, and the like. As FIG. 1 illustrates,particular embodiments may operate in conjunction with a networkenvironment comprising social network environment 20 and client devices30, as well as, in some embodiments, one or more third party webapplication servers 40 or one or more enterprise servers 50. Clientdevices 30, web application servers 40, and enterprise servers 50 may beoperably connected to the network environment and network cloud 60 via anetwork service provider, a wireless carrier, a set of routers ornetworking switches, or any other suitable means.

Each client device 30, web application server 40, or enterprise server50 may generally be a computer, computing system, or computing device(such as that described below with reference to FIG. 9) includingfunctionality for communicating (e.g., remotely) over a computernetwork. Client device 30 in particular may be a desktop computer,laptop computer, personal digital assistant (PDA), in- or out-of-carnavigation system, smart phone or other cellular or mobile device, ormobile gaming device, among other suitable computing devices. Clientdevice 30 may execute one or more client applications, such as a webbrowser 202 (e.g., MICROSOFT WINDOWS INTERNET EXPLORER, MOZILLA FIREFOX,APPLE SAFARI, GOOGLE CHROME, AND OPERA, etc.), as illustrated in FIG.2B, to access and view content over a computer network 60. In particularimplementations, the client applications allow a user of client device30 to enter addresses of specific network resources to be retrieved,such as resources hosted by social network environment 20, webapplication servers 40, or enterprise servers 50. These addresses can beUniform Resource Locators (URLs). In addition, once a page or otherresource has been retrieved, the client applications may provide accessto other pages or records when the user “clicks” on hyperlinks to otherresources. By way of example, such hyperlinks may be located within theweb pages and provide an automated way for the user to enter the URL ofanother page and to retrieve that page.

More particularly, when a user at a client device 30 desires to view aparticular web page (hereinafter also referred to as a target structureddocument) hosted by social network environment 20, or a web applicationhosted by a web application server 40 and made available in conjunctionwith social network environment 20, the user's web browser 202, or otherclient-side structured document rendering engine or suitable clientapplication, formulates and transmits a request to social networkenvironment 20. The request generally includes a URL or other documentidentifier as well as metadata or other information. By way of example,the request may include information identifying the user, such as a userID, as well as information identifying or characterizing the web browser202 or operating system running on the user's client computing device30. The request may also include location information identifying ageographic location of the user's client device or a logical networklocation of the user's client device, as well as timestamp identifyingwhen the request was transmitted.

In an example implementation, when a request for a web page orstructured document hosted by social network environment 20 is receivedby the social network environment 20, one or more page-generatingprocesses 200 executing within the social network environment 20typically generate a base web page in the form of a Hyper Text MarkupLanguage (HTML), Extensible Markup Language (XML), or other webbrowser-supported structured document. The generated structured documentis then transmitted in a response, which may comprise one or moreportions or partial responses, to the requesting client 30 via aHypertext Transfer Protocol (HTTP) or other suitable connection forrendering by the web browser 202 at the client device 30. The structureddocument may include one or more resources (e.g. JavaScript scripts,code segments, or resources, Cascading Style Sheet (CSS) code segmentsor resources, image data or resources, video data or resources, etc.),or references to such resources, embedded within the transmitteddocument. By way of example, a resource embedded in an HTML document maygenerally be included or specified within a script element, imageelement, or object element, among others, depending on the type ofresource. The element referencing or specifying the resource may includea source attribute (e.g., src) identifying a location of the resource,which may be within a server or data store within social networkenvironment 20 or at one or more external locations, to the clientdevice 30 requesting the web page. Typically, upon receipt of theresponse, the web browser 202 or other client document renderingapplication running at the client device 30 then constructs a documentobject model (DOM) representation of the received structured documentand requests the resource(s) (which may be at one or more other externallocations) embedded in the document.

In an example implementation, when a registered user of social networkenvironment 20 first requests a web page from social network environment20 in a given user session, the response transmitted to the user'sclient device 30 from social network environment 20 may include astructured document generated by page-generating process 200 forrendering a login page at the client device. The user may then enter hisor her user login credentials (e.g., user ID and password), which arethen transmitted from the user's client device 30 to social networkenvironment 20. Upon successful authentication of the user, socialnetwork environment 20 may then transmit a response to the user's webbrowser 202 at the user's client device 30 that includes a structureddocument generated by page-generating process 200 for rendering a userhomepage or user profile page at the user's client device. Furthermore,in particular embodiments, and as will be described below, this or asubsequent response may further include one or more executable codesegments (e.g., JavaScript) that, when received by the user's clientdevice 30, implement a frontend (client-side) typeahead process 204 thatexecutes in conjunction with the user's web browser 202.

In one example embodiment, social network environment 20 comprisescomputing systems that allow users at client devices 30 to communicateor otherwise interact with each other and access content, such as userprofiles, as described herein. Social network environment 20 is anetwork addressable system that, in various example embodiments,comprises one or more physical servers 22 a or 22 b (hereinafter alsoreferred to collectively as servers 22) as well as one or more datastores collectively referred to herein as data store 24 (which may beimplemented in or by one or more of a variety of consolidated ordistributed computing systems, databases, or data servers), asillustrated in FIG. 2A. The one or more physical servers 22 are operablyconnected to computer network 60 via, by way of example, a set ofrouters or networking switches 26. In an example embodiment, thefunctionality hosted by the one or more physical servers 22 may includeweb or HTTP servers, FTP servers, as well as, without limitation, webpages and applications implemented using Common Gateway Interface (CGI)script, PHP Hyper-text Preprocessor (PHP), Active Server Pages (ASP),Hyper Text Markup Language (HTML), Extensible Markup Language (XML),Java, JavaScript, Asynchronous JavaScript and XML (AJAX), and the like.

Physical servers 22 may host functionality directed to the operations ofsocial network environment 20. By way of example, social networkenvironment 20 may host a website that allows one or more users, at oneor more client devices 30, to view and post information, as well ascommunicate with one another via the website. Hereinafter, servers 22may be referred to as server 22, although, as just described, server 22may include numerous servers hosting, for example, social networkenvironment 20, as well as other content distribution servers, datastores, or databases. Data store 24 may store content and data relatingto, and enabling, operation of the social network environment as digitaldata objects including content objects. A data object, in a particularimplementation, is an item of digital information typically stored orembodied in a data file, database, or record. Content objects may takemany forms, including: text (e.g., ASCII, SGML, HTML), images (e.g.,jpeg, tif and gif), graphics (vector-based or bitmap), audio, video(e.g., mpeg), or other multimedia, and combinations thereof. Contentobject data may also include executable code objects (e.g., gamesexecutable within a browser window or frame), podcasts, etc. Logically,data store 24 corresponds to one or more of a variety of separate orintegrated databases, such as relational databases and object-orienteddatabases, that maintain information as an integrated collection oflogically related records or files stored on one or more physicalsystems. Structurally, data store 24 may generally include one or moreof a large class of data storage and management systems. In particularembodiments, data store 24 may be implemented by any suitable physicalsystem(s) including components, such as one or more database servers,mass storage media, media library systems, storage area networks, datastorage clouds, and the like. In one example embodiment, data store 24includes one or more servers, databases (e.g., MySQL), and/or datawarehouses.

Data store 24 may include data associated with different social networkenvironment 20 users, client devices 30, web application servers 40, orenterprise servers 50, as well as, in particular embodiments, dataassociated with various concepts. As described above, particularembodiments relate to a social network environment 20 that includes aplatform enabling an integrated social network environment. In thefollowing example embodiments, the social network environment may bedescribed or implemented in terms of a social graph including socialgraph information. In particular embodiments, data store 24 includes asocial graph database 206 in which the social graph information for usein implementing the social network environment described herein isstored. In particular embodiments, the social graph information storedby social network environment 20 in data store 24, and particularly insocial graph database 206, includes a plurality of nodes and a pluralityof edges that define connections between corresponding nodes. Inparticular embodiments, the nodes or edges themselves are data objectsthat include the identifiers, attributes, and information (including theinformation for their corresponding profile pages) for theircorresponding users or concepts (as described below), some of which isactually rendered on corresponding profile or other pages. The nodes mayalso include pointers or references to other objects, data structures,or resources for use in rendering content in conjunction with therendering of the profile pages corresponding to the respective nodes.

FIG. 3 illustrates an example social graph 300 shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, the plurality of nodes and edges of social graph 300 arestored as data objects in data store 24, and particularly social graphdatabase 206, as described above. Additionally, as will be describedlater, data store 24 may further include one or more searchable orqueryable indexes of nodes or edges generated by indexing social graphdatabase 206. In particular embodiments, the plurality of nodes includesa first set of administered nodes 302 and a second set ofun-administered nodes 304. In particular embodiments, the first set ofadministered nodes 302 are user-administered nodes (hereinafter alsoreferred to as “user nodes”) that each correspond to a respective userand a respective user profile page of that user. In particularembodiments, user profile pages corresponding to user nodes 304 may bemodified, written to, or otherwise administered by, and only by, theirrespective owner (registered) users (unless an official administrator ofsocial network environment 20 in general desires or requires access tomodify or delete a user's profile page, e.g., as a result of scrupulousor otherwise inappropriate action on the part of the registered user).In one particular embodiment, the first set of user nodes 302 includes afirst subset of authenticated nodes 302 a and a second subset ofun-authenticated nodes 302 b. In a particular embodiment, the firstsubset of authenticated nodes 302 a correspond to respective registeredauthenticated users while the second subset of un-authenticated nodes302 b correspond to registered users who have not been authenticated bysocial network environment. For example, an authenticated user may be auser who has been verified to be who they claim to be in his or herrespective profile page while an un-authenticated user may be a user whohas not been verified to be who they claim to be in his or herrespective profile page (e.g., an un-authenticated user may register aprofile page in President Barack Obama's name, although theun-authenticated user is not President Obama). In some embodiments, forsome existing user profile pages, social network environment 20 maydetermine whether the administrator of the user profile page is trulythe authentic voice of the claimed user (real person the user claims tobe). If it is determined that the current administrator is not theauthentic or true claimed user, social network environment 20 may removethe user's administrative rights to the page. In this way, the user nodeand corresponding user profile page may be redefined in the social graphinformation stored in social graph database 206 as a concept node 304and corresponding concept profile page as will be described later. Itshould further be noted that, in various example embodiments, user nodes302 a and 302 b may or may not be classified distinctly as differentnode types; that is, in one embodiment, a user node 302 may beidentified as an authenticated user node or an un-authenticated usernode based on the data stored with or within the data objectcorresponding to the node rather than by an explicit user node type orsub-type.

FIG. 4A illustrates an example user profile page of a user correspondingto a user node 302. In particular embodiments, a user profile page isvisible to the user, the user's friends, and even other non-friend usersdepending on privacy settings, which may be set or modified by the uservia the user's profile page or a user homepage, for example. The userprofile page may comprise a number of different subpages viewable oraccessible via selecting one or more tabs 401. By way of example, in theembodiment illustrated in FIG. 4A, the user profile page includes a Wall(feed) tab 401 a for accessing a wall (feed) for postings (describedbelow), an Info tab 401 b for entering and displaying information aboutor related to the user, a Photos tab 401 c for uploading and displayingphotos, and a Boxes tab 401 d. A user may select a particular photo orpicture uploaded in photos tab 401 c for display as a user profilepicture 403. In an example implementation, the user's profile picture403 as well as other features such as, for example, the options to senda message to another user, edit the profile page, view friends of theuser, or view photos of the user, may be displayed in a “chrome”(border) region of the page no matter which of tabs 401 is selected. Insome implementations, a search bar or search interface is also renderedin the chrome of a user profile page (as well as other pages) enablingusers to type in information such as names of other users or conceptsthe user desires to search for.

Generally, a great portion of, or all of, the information accessible orvisible to the user and other users via the user profile page isself-declared; that is, the user types or otherwise enters informationor content in various sections or forms that may or may notautomatically appear by default when the user profile page is created.In particular embodiments, a user may edit his or her user profile pageat anytime the user is logged into social network environment 20. By wayof example, user profiles include data that describe the respectiveusers of the social network enabled by social network environment 20,which may include, for example, proper names (first, middle and last ofa person, a trade name or company name of a business entity, etc.)biographic, demographic, and other types of descriptive information in abasic information section 402 under Info tab 401 b. The basicinformation section 402 may further include a user's sex, current cityof residence, birthday, hometown, relationship status, political views,what the user is looking for or how the user is using the social network(e.g., for looking for friendships, relationships, dating, networking,etc.), and the like.

In particular embodiments, a user profile page may also include apersonal information section 406 where the user can enter more personaldeclarations. By way of example, a personal information section 406 mayinclude a sub-section 408 in which the user may declare variousactivities he, she, or it participates in or enjoys such as, forexample, sports or music. For example, in section 408, the user maydeclare these activities by, for example, simply listing the activities.For example, the user may list “weight lifting, hiking, playingpingpong, and foozball,” or may use phrases such as, for example, “Ienjoy weightlifting, I like hiking, I love playing pingpong, I'm good atfoozball.” The user may separate or delineate his or her declaredactivities (and other declarations described below) with, for example,commas, semicolons, dashes, or carriage returns (which may berecognizable by the typeahead or bootstrapping processes describedbelow). An example personal information section 406 may also include asub-section 410 in which the user may declare various interests. Again,the user may simply list such interests, such as by typing, for example,“reading and photography,” or by using phrases such as, for example, “Ilike to read, I like photography.” As another example, interests section406 may include a favorite music sub-section 412 in which the user maydeclare music he or she likes or is interested in, a favorite TV showssub-section 414, a favorite movies sub-section 416, a favorite bookssub-section 418, a favorite quotations sub-section 420, and even ageneral “about me” sub-section 422 in which the user may enter generaldeclarations about himself or herself that may not fit under thepreviously described sections.

In particular embodiments, a user profile page may also include acontact information section 424 in which the user may enter variouscontact information including, for example, email addresses, phonenumbers, and city of residence. A user profile page may also include aneducation and work section 426 in which the user may enter his or hereducational history. By way of example, a user may declare that he orshe attended Stanford University in section 426 by, for example, simplytyping “Stanford University,” by typing “I attended StanfordUniversity,” or by selecting Stanford University from a menu interface.The user may also describe more specific information, such as, forexample, the degree awarded, the field of the degree, the graduationdate, etc. As another example, section 426 may enable the user to enterthe user's work experience. By way of example, a user may declare thathe or she works at company Z by, for example, simply typing “Company Z,”by typing “I work at Company Z,” or selecting company Z from a menu.

In particular embodiments, as will be described in more detail later,one or more terms in declarations entered in one or more of thepreviously described sections or sub-sections may be highlighted,rendered in a different color, underlined, or clickable. By way ofexample, one or more terms entered as declarations, and particularlyterms matched to known concepts or existing concept nodes 304, may beassociated with a hyperlink that, when clicked or otherwise selected,directs the user to a concept profile page devoted to the term and, inparticular embodiments, having a name identical or similar to thedeclared term. By way of example, clicking on a hyperlink correspondingto “Family Guy” in favorite TV shows section 414 may direct the user toa web page (a concept profile page/hub as described below) devoted tothe Family Guy TV show.

In particular embodiments, a user profile page also includes a friendssection 428 (which may be visible in the chrome or other region of thepage) that displays all or a subset of the user's friends as defined byedges in the social graph stored in social graph database 206. Inparticular embodiments, the user may click on a name or thumbnail image429 associated with a friend resulting in the directing of the user tothe user profile page of the selected friend. In particular embodiments,any action that a user takes with respect to another second user,whether or not the second user may be a friend of the user or not, and,in particular embodiments, actions that the user takes with respect tovarious concept nodes, may be displayed in a recent activity section430, which may be viewable as a sub-section within a wall (feed) section432 under Wall (feed) tab 401 a. Generally, wall (feed) section 432 is aspace on every user's profile page that allows the user and friends topost messages via input box 434 for the user and friends to see, as wellas to comment or otherwise express themselves in relation to posts onthe wall (feed).

In particular embodiments, the second set of un-administered nodes 304are non-administered nodes that each correspond to a respective conceptand a respective concept profile page (also referred to hereinafter as a“hub”) devoted to the respective concept. In particular embodiments,un-administered nodes (also referred to as “concept nodes” or “hubnodes”) 304 are nodes having respective concept profile pages (hubs)that are generally not administered by any one user; rather, inparticular embodiments, hubs may generally be administered, created, andwritten and contributed to or modified by, at least in part, by anyregistered user of social network environment 20, including, inparticular embodiments, users not having connections with the hub nodes304 (that is, users whose user nodes 302 are not necessarily connectedwith the hub nodes 304 with edges in the social graph in social graphdatabase 206). In a sense, hubs may be administered, or contributed to,by the community of registered users of social network environment 20.In particular embodiments, the second set of hub nodes 304 includes afirst subset of un-administered nodes 304 a that each correspond to anon-generic hub and a second subset of un-administered nodes 304 b thateach correspond to a generic hub. By way of example, a generic hub maybe a hub devoted to an abstract activity such as running while anon-generic hub may be a hub devoted to a more specific concept, such asa profile page devoted to a particular club of runners. It shouldfurther be noted that, in various example embodiments, nodes 304 a and304 b may or may not be classified distinctly as different node types;that is, in one embodiment, a hub node 304 may be identified as ageneric hub node or a non-generic hub node based on the data stored withor within the data object corresponding to the hub node rather than byan explicit hub node type or sub-type.

Similar to user profile pages, concept profile pages (“hubs”) shareinformation related to the concept associated with the corresponding hubnode 304. In particular embodiments, any registered user logged in tosocial network environment 20 and viewing a hub may add content to thehub similar to a wiki-site. FIG. 5 illustrates an example hub for themovie, “The Shawshank Redemption.” In an example embodiment, and asillustrated in FIG. 5, a hub may include sub-pages accessible via wall(feed) tab 501 a, info tab 501 b, photos tab 501 c, and boxes tab 501 dsimilar to a user profile page. A hub may also generally include a basicinformation section 502, a detailed info section 504, as well as,potentially, other sections, any and all of which may generally befilled in by any user viewing the hub (although in particularembodiments, there may be a time delay associated with a contentapproval or synchronization process before the user-generated oruser-added content is visible in the hub) or, additionally oralternately, based on extracting information from external orthird-party sources (e.g., WIKIPEDIA). A hub may also include a photo orpicture section under photos tab 501 c allowing users to upload imagesin or related to the concept, one of which may be selected as a profilepicture 512 for the hub.

In particular embodiments, wall (or news feed/activities feed) section501 a, or other feed or activities section of the hub, displayscomments, status updates, wall posts and other user activitiesassociated with the user and friends of the user that are viewing thehub. The wall (or news feed/activities feed) section 501 a, or otherfeed or activities section of the hub may also display comments, statusupdates, wall posts and other user activities and user generated contentthat are related to the concept for which the hub was created. Moreparticularly, one or more processes within social networking environment20 may perform a search on comments, status updates, wall posts andother user-generated content and user activities associated with therequesting user and friends of the requesting user filtered by concept;that is, a keyword search for keywords related to the concept of thecurrently requested or viewed hub (and potentially keywords related tothe concepts associated with the recommended hubs) in these streams ofuser feeds or activities related to the requesting user and therequesting user's friends, and display this subset of user content oractivities in the wall or feed section 501 a of the currently requestedor viewed hub. By way of example, U.S. patent application Ser. No.12/704,400, filed 11 Feb. 2010, and titled REAL TIME CONTENT SEARCHINGIN SOCIAL NETWORK, describes methods, processes, or systems forperforming such searching, filtering, and displaying, and is herebyincorporated by reference herein. Wall or feed section 501 a may alsoinclude a section, which may be a separate section from that justdescribed, that displays comments, status updates, wall posts and otheruser activities of any and all users of social networking environment 20that are related to the concept for which the hub was created, not justthose of the user and friends of the user viewing the hub.

In particular embodiments, the default sections displayed in aparticular hub upon creation of the hub may depend on the conceptitself; that is, hub nodes 304 may be categorized by social networkenvironment 20, and these categories (e.g., people, places, things,activities, sports, sports teams, celebrities, cities, locations,movies, actors, books, restaurants, etc.) may dictate, at least in part,which sections are displayed on a particular hub. By way of example, amovie hub may include a section or sub-section for entering actorsstarring in the movie, as illustrated in FIG. 5, as well as sections orsub-sections for entering information such as the director, writer,releasing studio, release date, etc. In particular embodiments, a hubalso includes a section 508 (which, in particular embodiments, may bevisible no matter which of tabs 501 are currently selected) that listsor displays users that have connections (and corresponding edges in thesocial graph) to or with the concept, such as a fans section 508 in theexample illustrated in FIG. 5. By way of example, such users may haveconnections, and associated edges stored in social graph database 206,indicating, for example, that they like the movie, saw the movie, wantto see the movie, acted in the movie, etc. In some embodiments, theusers displayed in fans section 508 may only include users who are alsofriends with the user currently viewing the hub.

In particular embodiments, each hub also includes a recommendationssection 510 (which, in particular embodiments, may be visible no matterwhich of tabs 501 are currently selected) that includes or displays alist or set of names 512, thumbnail images 514, or other identifiersassociated with other hubs, each of which may include a hyperlink to therespective other hub. In particular embodiments, the hubs displayed orlisted in recommendations section 510 have some determined relation to,or are determined based on leveraging information extracted from socialgraph database 206 about, one or more of: the particular user (alsoreferred to hereinafter as the “requesting user”) requesting orcurrently viewing the particular hub (also referred to hereinafter asthe “requested hub”), the requested hub, friends of the user whose usernodes 302 may or may not also be connected to the requested hub's hubnode 304 with respective edges, and other hubs having respective hubnodes 304 that are also connected to the requested hub's hub node 304.By way of example, the recommended hubs displayed in recommendationssection 510 may include hubs that are liked or otherwise connected (withedges in social graph database 206) to friends of the requesting user(as defined by edges in social graph database 206), and particularlyfriends that are also connected to the requested hub (with edges insocial graph database 206). As another example, the recommended hubsdisplayed in recommendations section 510 may include hubs that users,and particularly friends of the requesting user (as defined by edges insocial graph database 206), also like or are otherwise connected to(with edges in social graph database 206), but who aren't necessarilyconnected with the requested hub (with edges in social graph database206). As another example, the recommended hubs displayed inrecommendations section 510 may include hubs that are connected to therequested hub (with edges in social graph database 206) and one or morefriends of the requesting user (as defined by edges in social graphdatabase 206). As another example, the recommended hubs displayed inrecommendations section 510 may include hubs that are connected to therequested hub (with edges in social graph database 206) but that aren'tnecessarily connected with friends of the requesting user (as defined byedges in social graph database 206).

By way of example, a recommendations section 510 for a hub correspondingto a movie may display hubs corresponding to movies that are directed bythe same director, movies sharing some of the same actors, movies of thesame genre, or movies liked by friends of the user, etc. FIG. 9,described in detail below, shows a flowchart illustrating an examplemethod for generating one or more recommendations, and particularlyrecommended hubs, for display to a user currently viewing a hub based atleast in part on information extracted from the social graph database206. Generally, as described above, one goal or motivation fordisplaying recommended hubs to the requesting user currently viewing orrequesting a particular hub is to provide the user with recommended hubsthe user may be interested in viewing or interacting with and,furthermore, facilitate navigation to such recommended hubs from thecurrently viewed hub and, in particular embodiments, facilitate thecreation of edges connecting the user to one or more of the recommendedhubs the user demonstrates or indicates an interest in.

In particular embodiments, hub nodes 304 and their respective hubs maybe explicitly created by users of social network environment 20 orgenerated automatically based on various criteria as described belowwith reference to the example flowcharts illustrated in FIGS. 6 and 7and the example web pages illustrated in FIGS. 4C-4D. In particularembodiments, hubs and their respective hub nodes 304 may be of twovarieties such as, for example, whether or not they are considered orclassified as generic or non-generic. In one particular implementation,hubs and their respective hub nodes 304 may be “locked” or“un-locked.”Hubs may be locked at the time of creation, or othersuitable time, by, for example, the creator or an administrator ofsocial network environment 20. As described above, hubs are essentiallycommunity owned, and hence, in particular embodiments, any user ofsocial network environment 20 may edit (e.g., add content ordeclarations to) hubs. However, in particular embodiments, edits inun-locked hubs may “go live” (become visible to the user or other usersviewing the hub) immediately while edits in locked hubs may requireapproval by trusted users or administrators before being modified andpresented publicly to users. Additionally, it should be noted that, insome embodiments, social network environment 20 may track which usersadded which content to hubs as well as when these users added therespective content.

It should also be noted that, in particular embodiments, social networkenvironment 20 provides means or processes (e.g., selectable links oruser interfaces) for the true voices of hubs corresponding to hub nodes304 (or un-authenticated user profile pages corresponding toun-authenticated user nodes 302 b), such as the actual celebrity orbusiness for which a hub node 304 has previously been created, to claimthese nodes thereby assuming administrative rights over them andredefining them in the social graph as, for example, registeredauthenticated user nodes 302 a (or, alternately, as authenticated hubnodes 304).

As illustrated in FIG. 3, user nodes 302 and hub nodes 304 stored insocial graph database 206 may be connected with one another via edges.As described above, in some embodiments, each edge may be classified orcharacterized by an edge type of a plurality of edge types that define,indicate, or characterize the connection between the pair of nodesconnected by the edge. By way of example, user nodes 302 may beconnected with one another via edges 306 of a first edge type. Inparticular embodiments, edges 306 define friendship or other socialrelationship connections between users (e.g., friends) associated withthe respective user nodes 302. Additionally, user nodes 302 may beconnected with concept nodes 304 via edges 308 of one or more secondedge types. By way of example, a user corresponding to a user node 302may make a declaration or otherwise indicate that he or she likes, is afan of, wants, or otherwise has an interest in or association with aconcept corresponding to a particular hub node 304. The user mayindicate this like or interest via clicking a link on the correspondingconcept node's hub or by other suitable means, such as for example,clicking a link in the user's home or profile page in response to aninvitation, clicking a link in a friend's profile page, or, inparticular embodiments, by some automatic or automated means.

By way of example, as will be described in more detail below, FIG. 6illustrates an example method for automatically generating nodes andedges based on information currently being entered by a user of a socialnetwork environment, while FIG. 7 illustrates an example method forautomatically generating nodes and edges based on information previouslyentered by users of a social network environment. More particularly, forexample, an edge to an existing hub node 304 (or other user node 302)may be automatically created as a result of matching informationcurrently being entered by a user such as, for example, as the user istyping or otherwise entering a declaration in, for example, the user'sprofile page, as well as by mining information previously entered by auser in, for example, the user's profile page. As a result an edge 308may be defined and stored in social graph database 206 indicating theparticular form of the connection and the nodes connected by the edge.Furthermore, as will be described below, in cases in which the mined orcurrently entered information doesn't match an existing node stored insocial graph database 206, a new node may be created in social graphdatabase 206 as well as a new edge from the respective user's node tothe new node.

Furthermore, in some embodiments, various hub nodes 304 may be connectedwith one another in social graph database 206 via edges 310 of a thirdedge type. This third edge type may define an informational orcategorical relationship between hub nodes 304, some of which may tendto organize such hubs into hierarchies. By way of example, a generic hubdevoted to Asian food may have a link in the page to various Asianrestaurants or review pages displayed in non-generic hubs. As such, inthe social graph, edges 310 may connect the generic Asian food hub toone or more other generic hubs or non-generic hubs.

Additionally, in some embodiments, each edge type may include aplurality of edge sub-types that add more detail or metadata describingthe specific type of connection between corresponding pairs of nodes.Furthermore, in some embodiments, new edge types may be defined orgenerated automatically or dynamically. By way of example, informationentered into, or in relation to, third party web applications may causenew edge types to be defined and generated. As a particular example, aweb application for Netflix may result in an edge type that signifies“movies I want to see.”

In such embodiments in which edges have or are assigned associated edgetypes, the edge itself may store, or be stored with, data that defines atype of connection between the pair of nodes the edge connects, such as,for example, data describing the types of the nodes the edge connects(e.g., user, hub, category or classification of hub), access privilegesof an administrator of one of the pair of nodes connected by the edgewith respect to the other node the edge connects to (e.g., read or writeaccess of an administrator of one node with respect to the other nodeconnected by the edge), or data describing how or why the edge was firstinitialized or created (e.g., in response to an explicit user action ordeclaration, or automatically without an explicit user action), thestrength of the connection as determined by various factors or criteriarelated to or shared by the nodes (or more particularly the users orconcepts associated with the respective connected nodes) connected bythe edge, among other suitable or relevant data.

In an alternate embodiment, each edge may simply define or represent aconnection between nodes regardless of the types of nodes the edgeconnects; that is, the edge itself may store, or be stored with,identifiers of the nodes the edge connects but may not store, or bestored with, data that describes a type of connection between the pairof nodes the edge connects. Furthermore, in any of these or otherembodiments, data that may indicate the type of connection orrelationship between nodes connected by an edge may be stored with thenodes themselves. In particular embodiments, the edges, as well asattributes (e.g., edge type and node identifiers corresponding to thenodes connected by the edge), metadata, or other information defining,characterizing, or related to the edges, may be stored (e.g., as dataobjects) in social graph database 206 and updated periodically or inresponse to various actions or factors (e.g., as a user interacts morewith a hub, the edge connecting the respective user and hub nodes may beupdated to reflect this interaction, which may then contribute to anaffinity or connection strength score characterizing the edge asdescribed in more detail below).

In particular embodiments, social network environment 20 may leverageinformation extracted from both user nodes 302 as well as hub nodes 304for various purposes or to implement or augment various existing or newfeatures. Additionally, as a hub node 304 is populated with informationentered or contributed by various users, other hub nodes 304 andrespective hubs may be generated based on such information as describedbelow. Furthermore, hubs may provide value to users in a number ofmanners. By way of example, if a first user visits a particular hub, theuser may discover that various ones of the user's friends are alsoconnected to that hub. The first user may also easily determine whatother hubs those friends are connected to. Social network environment 20may also correlate this information about the user and the user'sfriends to find, for example, overlapping interests or attributes, whichmay then be used to generate other hubs, used to generate targetedadvertisements, or to make recommendations to users, such as recommendedhubs, as described above and described in more detail below withreference to the flowchart shown in FIG. 9.

FIG. 6 shows a flowchart illustrating an example method forautomatically generating edges to existing hub nodes 304 (or user nodes302) as well as generating new hub nodes 304 and edges from user nodes302 (or hub nodes 304) to these newly generated hub nodes 304 based oninformation currently being entered by users of social networkenvironment 20. In particular embodiments, the automatic generation ofhubs and edges is based on data, and particularly text, such as forexample, declarations entered in the sections of user profile pages orhubs described above, as well as, in some embodiments, informationentered in other sections or forms of web pages hosted by social networkenvironment 20, including internal or external (e.g., third party) webapplications, messages sent between users, or wall (feed) postings. Inan example implementation, when a registered user of social networkenvironment 20 first requests a web page from social network environment20 in a given user session, the response transmitted to the user'sclient device 30 from social network environment 20 may include astructured document for rendering a login page at the client device. Theuser may then enter his or her user login credentials (e.g., user ID andpassword), which are then transmitted from the user's client device 30to social network environment 20. Upon successful authentication of theuser, social network environment 20 may then transmit a response to theuser's web browser 202 at the user's client device 30 that includes astructured document for rendering a user homepage or user profile pageat the user's client device. In particular embodiments, this or asubsequent response may further include one or more executable codesegments (e.g., JavaScript) that, when received by the user's clientdevice 30, implement a frontend (client-side) typeahead process 204 thatexecutes in conjunction with the user's web browser 202 or otherclient-side document rendering application, as illustrated in FIG. 2B.

In particular embodiments, as a user types or otherwise enters text intoa form used to add content or make declarations in various sections ofthe user's profile page or other page, the frontend typeahead process204 works in conjunction with one or more backend (server-side)typeahead processes 208 (hereinafter referred to simply as “backendtypeahead process 208”) executing at (or within) the social networkenvironment 20 (e.g., within servers 22), as illustrated in FIG. 2B, tointeractively and virtually instantaneously (as appearing to the user)attempt to auto-populate the form with a term or terms corresponding tonames of existing hubs, or terms associated with existing hubs,determined to be the most relevant or best match to the characters oftext entered by the user as the user enters the characters of text.Utilizing the social graph information in social graph database 206 orinformation extracted and indexed from social graph database 206,including information associated with nodes as well as edges, thefrontend and backend typeahead processes 204 and 208, in conjunctionwith the information from social graph database 206, as well aspotentially in conjunction with various others processes, applications,or databases located within or executing within social networkenvironment 20, are able to predict a user's intended declaration with ahigh degree of precision. However, social network environment 20 alsoprovides user's with the freedom to enter any declaration they wishenabling users to express themselves freely. As such, social networkenvironment 20 enables the creation of new hubs and corresponding hubnodes 304 related to virtually any concept.

Referring back to FIG. 4A, in particular embodiments, a user may edithis or her user profile page and make declarations by clicking orotherwise selecting an edit link 440 corresponding to the section thatthe user desires to edit or make a declaration. By way of example, FIG.4C illustrates the resultant rendered web page displayed to the user atthe user's client device 30 after the user has selected the edit link440 corresponding to the personal information section 406. As shown inFIG. 4C, a plurality of form boxes 409, 411, 413, 415, 417, 419, 421,and 423 are rendered enabling the user to type or otherwise enterdeclarations into corresponding sections 408, 410, 412, 414, 416, 418,420, and 422, respectively. As described above, as the user enters textcharacters into a form box, the frontend and backend typeahead processes204 and 208 attempt to identify existing hub nodes 304 (or user nodes304, e.g., especially user nodes corresponding to celebrities,businesses, or organizations) that match the string of charactersentered in the user's declaration as the user is entering thecharacters.

More particularly, referring to FIG. 6, as the user enters charactersinto a form box at 602, the frontend typeahead process 204 reads thestring of entered textual characters and, in particular embodiments, aseach keystroke is made, the frontend typeahead process 204 transmits theentered character string as a request (or call) at 604 to the backendtypeahead process 208 executing within social network environment 20. Inparticular embodiments, the frontend and backend typeahead processes 204and 208 may communicate via AJAX (Asynchronous JavaScript and XML) orother suitable techniques, and particularly, asynchronous techniques. Inone particular embodiment, the request is, or comprises, anXMLHTTPRequest enabling quick and dynamic sending and fetching ofresults. In particular embodiments, the frontend typeahead process 204also transmits before, after, or with the request at 604 a sectionidentifier (section ID) that identifies the particular section of theparticular page in which the user is making the declaration. In someembodiments, a user ID parameter may also be sent, but this may beunnecessary in some embodiments, as the user is already “known” based onhe or she logging into social network environment 20. It should also benoted that, although these and other steps of FIG. 6 may be described asoccurring as “single steps” and in a particular order, it should beappreciated that, since the frontend typeahead process 204 may continueto transmit strings of characters to social network environment 20 aseach keystroke is entered by the user, steps 602 and 604, and othersteps described below, may generally be repeated numerous times andpotentially in parallel as a user is entering a declaration or otherinformation.

In particular embodiments, as the backend typeahead process 208 receivesrequests or calls at 606 including a string of user-entered characterdata and section identifier, the backend process 208 performs, or causesto be performed (e.g., in conjunction with one or more other searchprocesses executing at social network environment 20), a string searchat 608 to identify existing nodes, and particularly hub nodes 304,having respective names or other hub identifiers matching the enteredtext, and in particular embodiments, matching a particular category ofnodes in social graph database 206 as determined, at least in part, bythe particular section identifier. In various example embodiments, thegranularity of the categories may vary. By way of example, in someembodiments, hubs corresponding to actors, directors, producers, movietypes or genres, may all be grouped in a “movie” category while in otherembodiments, each of these examples may represent their own category.Similarly, in some embodiments, hubs corresponding to football,basketball, soccer, rugby, and tennis may all be grouped in a “sports”category while in other embodiments, each of these may represent theirown category. In one embodiment, the backend typeahead process 208performs string matching; that is, the backend typeahead process 208attempts to match the latest string of characters received from thefrontend typeahead process 204 to an index of strings each correspondingto a name of a node in social graph database 206. In particularembodiments, the index of strings is updated periodically or as nodes302 and 304 are added to the social graph database 206 or other indexgenerated from social graph database 206. The backend typeahead process208 may use one or more of a variety of factors when attempting to matchthe string of entered text and as such may examine one or more of avariety of different aspects or attributes of existing nodes in socialgraph database 206. By way of example, in addition to attempting tomatch the entered text to names (name strings) of existing nodes, thebackend typeahead process 208 may use the section identifier todetermine a category of the declaration which may be then used to searcha subset of existing hub nodes 304 associated with the category. Inparticular embodiments, backend typeahead process 208 searches orqueries an index of nodes generated from social graph database 206 inwhich the nodes are indexed and searchable (or queryable) by hubcategory. The backend typeahead process 208 may also use informationabout the user entering the text including information entered in theuser's profile page, information about the users friends, informationabout other hub nodes 304 the user is connected with, etc. in order tobest match a user declaration to an existing concept and respective hubnode 304 (or user node 302). The backend typeahead process may alsoattempt to correct spellings or match to synonyms of the user-enteredcharacters or extrapolations of entered characters.

In particular embodiments, the backend typeahead process 208 may use oneor more matching algorithms to attempt to identify matching nodes. Inparticular embodiments, when a match or matches are found at 610, thebackend typeahead process 208 may transmit a response (which may utilizeAJAX or other suitable techniques) to the user's client device at 616that may include, for example, the names (name strings) of the matchingnodes as well as, potentially, other metadata associated with thematching nodes. By way of example, FIG. 4D illustrates the result of theuser entering the characters “wei” into form box 409 corresponding toactivities section 408. In the example illustrated in FIG. 4D, thefrontend typeahead process 204 displays a drop-down menu 442 thatdisplays names of matching existing hubs and respective hub nodes 304(e.g., a hub named or devoted to “weight lifting”), which the user canthen click on or otherwise select thereby confirming at 618 the desireto declare the matched concept name corresponding to the selected node.By way of example, upon clicking “weight lifting,” the frontendtypeahead process 204 auto-populates, or causes the web browser 202 toauto-populate, the form box 409 with the declaration “weight lifting” at620. In an alternate embodiment, the frontend typeahead process 204 maysimply auto-populate the form with the name or other identifier of thetop-ranked match rather than display a drop-down menu. In such anembodiment, the user may confirm the auto-populated declaration simplyby keying “enter” on his or her keyboard or by clicking on theauto-populated declaration.

In particular embodiments, upon user confirmation of the matching node,the frontend typeahead process 204 may transmit at 622 a request to thebackend typeahead process 208 that informs the backend typeahead processof the user's confirmation of the matched hub. In particularembodiments, in response to the request transmitted at 622, the backendtypeahead process may automatically (or alternately based on aninstruction in the request) call or otherwise instruct anedge-generating API (Application programming interface) 210 to create,at 624, an edge in the social graph stored in social graph database 206between the particular user's node 302 and the particular node(generally a hub node 304, but in some embodiments, possibly a user node302) corresponding to the confirmed declaration. In an alternateembodiment, the request transmitted at 622 may not be generated andtransmitted by the frontend typeahead process 204 until the user hasselected the save changes (or other submit) button 444 indicatingconfirmation of the user's desire to make the declaration (ordeclarations made in any and all of the displayed form boxes).

In particular embodiments, the node types (e.g., 302 a, 302 b, 304 a,and 304 b) or categories of existing nodes as, for example, determinedbased, at least in part, on the section category as identified by thecorresponding section identifier for the section in which thedeclaration was made (e.g., favorite movies) or based on social graphinformation stored in social graph database 206, are used by the backendtypeahead process 208 to better match a string of entered characters ofa declaration to existing nodes that may be candidates for matchingnodes. By way of example, consider an example in which a user types“jaguar” into a user profile section. In such an example, the backendtypeahead process 208 may identify numerous existing hubs havingcorresponding names that at least include the name “jaguar,” or aderivation thereof (e.g., “jaguars”). For example, the backend typeaheadprocess 208 may identify a hub node 304 devoted to the jungle catjaguar. The backend typeahead process 208 may also identify a hub node304 devoted to the Jacksonville Jaguars professional football team andstill another hub node 304 devoted to the Jaguar luxury and performancecar-maker. In such cases, all of these three hub nodes may be matched at610 by the backend typeahead process and hence, all three hub names maybe transmitted at 616 in some embodiments, while in other embodiments,the backend typeahead process 208 may only transmit one matching hubnode name that is determined to be the most relevant based, for exampleand as described above, on using the section ID or other parametersextracted from the user's profile to determine a category in which themost relevant matching hub node 304 would be indexed in.

Additionally, in some embodiments, as described below with reference toFIG. 9, other factors may also be used to determine the strength orrelevancy of the matching hub nodes 304 including, by way of example,the number of the user's friends having respective user nodes 302connected with a matching hub node 304, the number of total users havingrespective user nodes 302 connected with a matching hub node 304, thenumber of other hub nodes 304 connected with the matching hub node 304,information obtained by analyzing other hub nodes 304 connected to boththe user's node 302 and a matching hub node 304, or other hub nodes 304connected to nodes 302 corresponding to friends of the user as well asto a matching hub node 304. Moreover, as described below, informationcharacterizing the strength of the connections associated with the edgesconnecting any of these nodes may also be used to weight their relevancyin determining the most relevant matching hub node or nodes 304.

In particular embodiments, there are at least one or two determinationsthat are made by the backend typeahead process 208 before the frontendtypeahead process 204 auto-populates a form box with names correspondingto matched hubs and respective hub nodes 304 (or user nodes 302). First,considering the above example, in the case that a plurality of matchesto existing nodes are identified at 610, the backend typeahed processmay then determine at 612 a confidence score for each of the matchesthat indicates an absolute or relative quality of each of the names ofthe matching nodes, the quality of the matching nodes themselves, orotherwise a level of confidence that the backend process 208 has thatthe match is correct (the intended concept the user was entering ortrying to enter). This determination at 612 may also result or involve aranking of the matches (which may be reflected in the order of thematches displayed in the drop-down menu 442).

One or more of numerous factors may be used to determine a confidencescore, quality, or ranking of a matching node. By way of example, suchfactors may again include, as just described, the number of the user'sfriends having respective user nodes 302 connected with a matching hubnode 304, the number of total users having respective user nodes 302connected with a matching hub node 304, the number of other hub nodes304 connected with the matching hub node 304, information obtained byanalyzing other hub nodes 304 connected to both the user's node 302 anda matching hub node 304, or other hub nodes 304 connected to nodes 302corresponding to friends of the user as well as to a matching hub node304. Other suitable factors may also include the number of sections onthe corresponding candidate matching hub that are filled in, therelationship of content displayed on the hub corresponding to thematching hub node to the content, including other declarations,displayed on the user's profile page, etc. Again, as described below,information characterizing the strength of the connections associatedwith the edges connecting any of these nodes may also be used to weighttheir relevancy in determining the most relevant matching hub node ornodes 304. Referring back to the jaguar example, the backend typeaheadprocess 208 may identify another declaration of the user that says “Ilove watching football,” and as such, based on this identification (aswell as other factors), the backend typeahead process 208 may rank thenode corresponding to the Jacksonville Jaguars professional footballteam as the best match and the frontend typeahead process 204 may listthe name of this node at the top of the drop-down menu or automaticallyauto-populate the form with the name.

In particular embodiments, the backend typeahead process 208 may thenmake a second determination 614 before the frontend typeahead process204 auto-populates a form box with names corresponding to ranked matchednodes. By way of example, based on the confidence scores, which may havebeen determined at 612, one or both of the frontend and backendtypeahead processes 204 and 208 may determine whether there is adetermined level of certainty or confidence (a confidence score) foreach match before the match is displayed to the user in the form of adrop-down menu for selection or auto-populated in the form box. That is,in particular embodiments, even though one or more matches have beenidentified from the existing nodes in the social graph database 206,their respective certainties (in being the actual concept the user wasintending to declare) as demonstrated by their determined confidencescores may be below a first predetermined threshold, and hence, none ofthe matches may be displayed to the user and be auto-populated by thefrontend typeahead process 204. That is, rather than display and providethe user with the match or matches having confidence scores below thethreshold, the frontend typeahead process 204 may allow the user tofinish typing the declaration himself or herself, and then transmit therequest at 622. The backend process 208 may determine the best matchcorresponding to the user's declaration and proceed with calling theedge-generating API 210 to which it passes information about the user'snode and information about the existing node to the best matching node,resulting in the creation of an edge at 624 between the user's node andthe best matching node in the social graph database 206.

In alternate embodiments, determining a confidence score of each matchmay be performed as a part of the searching step 608 or determinationstep 610. In such embodiments, the determination of whether a match ormatches have been found may be based on comparing respective confidencescores determined for the prospective matches with a secondpredetermined threshold below the first predetermined thresholddescribed above. That is, the second predetermined threshold may be usedwhen determining if a match is found while the first predeterminedthreshold may be used when determining if the match should be autopopulated for display to the user.

In particular embodiments, the edge type of the edge created by theedge-generating API at 624 is based on one or more of the sectioncategory as identified by the corresponding section identifier for thesection in which the declaration was made, or the category of theconfirmed matched hub as determined by the backend process based onsocial graph information stored in data store 24. By way of example,consider another example in which a user is making a declaration aboutStanford University. Also consider, for this example, that there is anexisting hub node 304 devoted to Stanford University. A first exampleuser may enter a declaration that he or she attends or attended StanfordUniversity as a student thereby resulting in the creation of an edge ofone edge type or sub-type from the first user's node 302 to the hub node304 for Stanford University. A second example user may enter adeclaration that he or she teaches or taught at Stanford Universitythereby resulting in the creation of an edge of another edge type orsub-type from the second user's node 302 to the hub node 304 forStanford University. Still further, a third example user may enter adeclaration that he or she likes or is a fan of Stanford Universitythereby resulting in the creation of an edge of another edge type orsub-type from the third user's node 302 to the hub node 304 for StanfordUniversity. Thus, in this example, the type of edge created by theedge-generating API may be based on a word or words, and particularly averb (e.g., attends, teaches, likes, etc), entered with the declarationbut not part of the node name. Hence, in general and as evidenced bythis example, a multitude of factors may be used in, first, finding amatching node, and second, determining the appropriate edge type betweenthe user's node and the matching node, including the section identifier,the words used in the declaration, and information about the user.

In particular embodiments, if no suitable match is identified at 610 toa predetermined level of certainty (e.g., based on comparison ofconfidence scores with the second threshold), or the user abstains fromselecting a provided or autopopoulated match, then, as the usercontinues to enter characters of text in a declaration, the frontendtypeahead process 204 waits until the user is finished entering thedeclaration as, for example, indicated by the user clicking or otherwiseselecting the save changes button 444, before transmitting the characterstring, section identifier, or other information/data at 626 to backendtypeahead process 208, which then calls a node-generating API 212 (whichmay include or be a part of the edge-generating API 210 described above)to which the backend typeahead process 208 passes the character string,section identifier, or other information/data. The backend typeaheadprocess 208 or node-generating API 212 may perform some pre-processingat 628 before generating a new node (e.g., generally a hub node 304). Byway of example, assuming the user entered “I love climbing in northernCalifornia” in, for example, form box 409 corresponding to activitiessection 408 of his or her user profile page. While the backend process208 may or may not have found matching existing hubs for some of theterms in the user's declaration (e.g., “climbing, California,” etc.),the backend process may determine that the qualities or confidencescores associated with these individual matched nodes are lower than thesecond predetermined threshold and that no existing nodes exist for themore particular declaration “I love climbing in northern California,”and hence, backend typeahead process 208 may determine that a new nodeshould be created; that is, a concept node 304 for, and named, “I loveclimbing in northern California.” The node-generating API 212 thengenerates the new node (a hub node 304 in this example) and acorresponding hub (with appropriate default sections) at 630. Theedge-generating API 210 may then create an edge between the new node andthe user's node at 632 in social graph database 206. As mentioned above,the backend typeahead process 208 or node-generating API 212 may performsome pre-processing at 628 before generating a new node. For example,the node-generating API may attempt to correct misspellings, removedelimiters (e.g., commas), remove articles (e.g., “a,” “the,” “and,”etc.), or remove other words including verbs. By way of example, suchprocessing may result in the creation of a new node simply called“Climbing in northern California,” as opposed to “I love Climbing innorthern California.” Such preprocessing may also be performed before orduring the matching step at 610 to facilitate the identification of truematches.

In particular embodiments, similar typeahead processes can be used tocreate edges between existing hub nodes 304 as well as to create new hubnodes 304 and edges from existing hub nodes 304 to such new hub nodes304 as users “fill-in” and otherwise upload content to hubscorresponding to existing hub nodes 304.

FIG. 7 shows a flowchart illustrating an example method, implemented atleast in part by a “bootstrapping feature” or “bootstrapping process”214, for automatically generating edges to existing hub nodes 304 (oruser nodes 302) as well as generating new hub nodes 304 and edges fromuser nodes 302 (or hub nodes 304) to these newly-generated hub nodes 304based on information previously entered by users of social networkenvironment 20, as well as, in some embodiments, information obtainedfrom one or more internal or external information sources or datarepositories. In contrast to the typeahead processes 204 and 208described with reference to the flowchart of FIG. 6, which may beconsidered “online” processes in that these typeahead process executewhen a user is logged into social network environment 20, thebootstrapping process 214 described with reference to the flowchart ofFIG. 7 may be considered an “offline” process in that it may execute atanytime whether or not any users of social network environment 20 arelogged on. In particular embodiments, the reason for this difference isthat the generation of nodes and edges based on previously enteredinformation may, in one embodiment, only be performed once over allusers and existing nodes of the social network environment 20. In suchan embodiment, after the bootstrapping process 214 has processed all thedesired information from all the desired users or concepts associatedwith existing nodes and generated new nodes and edges based on theprocessed information, the typeahead processes 204 and 208 describedabove with reference to FIG. 6 may then be used to generate any newnodes and edges.

In particular embodiments, the automatic generation of hubs and edges asa result of executing the bootstrapping process 214 is based on data,and particularly text, such as for example, declarations entered in theabove-described sections of user profile pages or hubs, as well as, insome embodiments, information entered in other sections or forms of webpages hosted by social network environment 20, including internal orexternal (e.g., third party) web applications, messages sent betweenusers, wall (feed) postings, and generally, any form, communication, orfeed in which text has been entered.

In particular embodiments, the method begins at 702 with bootstrappingprocess 214 scanning data structure 24, including social graph database206, for text entered or otherwise associated with or stored with eachuser of social network environment 20. As described above, all of theinformation about or associated with a given user including that enteredand displayed with the user's profile page may be stored with the user'snode 302 in social graph database 206. In particular embodiments, foreach user (but also, in some embodiments, potentially each hub having acorresponding hub node 304), bootstrapping process 214 identifies all ofthe fields or objects associated with the user's node 302 that containtextual characters at 704. As described above, such fields may includeany of sections 408, 410, 412, 414, 416, 418, 420, and 422 in the user'sprofile page as well as, in some embodiments, text in private messagessent between the user and other users, public messages posted in wall(feed) sections, status updates, captions below photos, etc.

In particular embodiments, for each field or object containing text,bootstrapping process 214 performs, at 706, some amount ofpre-processing of the text. By way of example, in particularembodiments, all the text in a given field is considered a singlecharacter string. In particular embodiments, pre-processing of thecharacter string at 706 includes applying a set of one or more heuristicrules to parse, separate, or delimit the character string into separatewords or phrases associated with distinct concept candidates. Moreparticularly, pre-processing may involve bootstrapping process 214separating the character string in a given field by delimiters (e.g.,commas, semicolons, carriage returns, etc.). By way of example, thecharacter string illustrated in section 408 of FIG. 4A may be delimitedinto four distinct concept or hub candidates, the first distinct hubcandidate being “weight lifting,” the second distinct hub candidatebeing “hiking,” the third distinct hub candidate being “playing pingpong,” and the fourth distinct hub candidate being “foozball.”Pre-processing at 706 may additionally involve identifying synonyms ofthe word or words in each distinct hub candidate, identifying words thatmay be misspelled, identifying the potentially correct spellings,expanding phrases or adding words to phrases (e.g., words that may havebeen unintentionally left out or left our as a result of brevity),removing URLs, removing metadata, normalizing the word or words based onlanguage (e.g., converting words from the language in which they wereentered into the language typically used by the user, or convertingwords from the language in which they were entered into the language inwhich a best match for the hub candidate is likely to be found), and thelike. By way of example, consider that a user may have entered“Godfather I, II, III” in a favorite movies section 416. In particularembodiments, bootstrapping process 214 possesses the intelligence orcapability to identify that the user has actually identified threemovies: The Godfather part I, The Godfather part II, and The Godfatherpart III. In such a case, the bootstrapping process 214 may considereach movie as a separate hub candidate as though the user had explicitlytyped out all three movie names.

In particular embodiments, each identified distinct hub candidate (e.g.,a string of characters) may then, at 708, be matched to or compared witha list of known concepts using one or more of a variety of suitablestring matching algorithms. In particular embodiments, the knownconcepts with which the hub candidates are compared may be indexed inthe form of corresponding strings of characters and stored in conceptdatabase 216. Generally, concept database may be an indexed repositoryof concept information that bootstrapping process 214 can query againstfor matching candidate hubs to known concepts. That is, in particularembodiments, it is desired to match each hub candidate with a singleknown concept. In particular embodiments, concept database 216 may bepopulated with known concepts by crawling one or more externalinformation sources or data repositories such as, for example, bycrawling WIKIPEDIA (www.wikipedia.org) or FREEBASE (www.freebase.com)and combining these crawling results with each other, as well as,potentially, information extracted from one or more internal informationsources, including social graph database 206. In one example embodiment,the concepts indexed and stored in concept database 216 don'tnecessarily have corresponding existing hub nodes 304 (or user nodes302) in social graph database 206. That is, concept database 216 maygenerally store an index of known concepts (each represented bycorresponding character string), as well as information about theseconcepts (which may be crawled from an external data source such asthose just described), but not all of these known concepts may havecorresponding existing hub nodes 304 stored in social graph database206. In an alternate embodiment, concept data base 216 may includesocial graph database 206 or vice versa. In particular embodiments, tofacilitate the matching of hub candidates to known concepts indexed inconcept database 216, the index of known concepts in concept database216 may be organized into categories such as, by way of example and notby way of limitation, people, places, things, activities, sports, sportsteams, celebrities, cities, locations, movies, books, restaurants, etc.The particular categories searched by bootstrapping process 214 may bedetermined by a section ID or other field identifier associated withwhere the hub candidate was identified.

In particular embodiments, as a result of pre-processing at 706, eachhub candidate may have associated with it, one or more character stringsthat are each attempted to be matched with known concepts in conceptdatabase 216. By way of example, the delimited character stringcorresponding to the user's entered text may be matched at 708 as wellas other character strings in which spelling changes, word additions,work removals, among other changes have been made. In particularembodiments, bootstrapping process 214 then identifies, at 710, a“shortlist” of the best matching known concepts matching the hubcandidate. In particular embodiments, bootstrapping process 214 thengenerates or determines, at 712, a confidence score or value for eachmatch in the shortlist (similarly to the confidence score described withreference to the typeahead processes and the flowchart of FIG. 6). Byway of example, the confidence score for each known concept may be basedon one or more of the following: a determination of how well the text inthe character string of the hub candidate matched the text in thecharacter string of each known concept, whether the spellings of any ofthe words in the hub candidate character string were changed to obtainthe match, whether any words were added or removed in the hub candidatecharacter string to obtain the match, etc.

As an example, when a shortlist of potential candidates is identified,in one embodiment, the shortlist is narrowed further by using otherinformation known about the user. Consider a user A who wrote declared“Twilight, Harry Potter.” If bootstrapping process 214 could notunambiguously identify the movie corresponding to “Twilight,” (e.g.,because there are multiple movies with the word “twilight”),bootstrapping process 214 may use the fact that many other users wholike “Harry Potter” also like the movie “Twilight: New Moon” (anunambiguous movie), and therefore determine that user A is referring to“Twilight: New Moon” with a sufficient degree of certainty when user Ahas typed “Twilight.” Similarly, bootstrapping process 214 could usedemographic information about the user. For example, an older user mayprefer the 1958 version of “Romeo and Juliet,” whereas a younger usermay be more likely to mean the newer version of “Romeo+Juliet” releasedin 1996.

In particular embodiments, bootstrapping process 214 then re-queries theconcept database 216 at 714 to find matches having the same confidencescores as the matches in the shortlist of matches. One motivation to dothis second round of matching is to eliminate the possibility of falsepositives. By way of example, rather than simply choose the match havingthe best confidence score, which may appear high thereby indicating ahigh level of confidence in the match, by performing the second round ofmatching, bootstrapping process 214 may find that there are numerousmatching known concepts having the same confidence score, therebysignaling the reality that the quality of, or confidence in, the matchis misleading and the match shouldn't be accepted.

Based on the results of 712 and 714, bootstrapping process 214 thendetermines, at 716, whether or not a suitable match to a known conceptexists for the given hub candidate. In particular embodiments, if it isdetermined at 716 that a match exists in concept database 716,bootstrapping process 214 then determines, at 718, whether or not anexisting node (e.g., usually a hub node 304 but also potentially a usernode 302) exists in social graph database 206 (In an alternateembodiment, bootstrapping process 214 may attempt to match a hubcandidate to an existing node in social graph database 206 first beforeresorting to attempting to match the hub candidate to known concepts nothaving corresponding existing nodes in social graph database 206). If itis determined at 718 that the matching known concept has a correspondinghub node 304 (or user node 302) in social graph database 206, thenbootstrapping process 214 may then call edge-generating API 210 at 720,which then generates, at 722, an edge between the user node 302 from orfor which the hub candidate was identified to the hub node 304 or (usernode 302) corresponding to the matched known concept. In contrast, if itis determined at 718 that a corresponding hub node 304 (or user node302) in social graph database 206 does not already exist for thematching known concept, then bootstrapping process 214 may then callnode-generating API 212 at 724, which then generates, at 726, a hub node304 (or user node 302) for the matching known concept. Bootstrappingprocess 214 may then call edge-generating API 210 at 728, which thengenerates, at 730, an edge between the user node 302 from or for whichthe hub candidate was identified to the newly created hub node 304 or(user node 302) corresponding to the matched known concept.

In particular embodiments, if it is determined that a match cannot befound for the character string associated with the hub candidate, atleast according to a desired or predetermined level of confidence,bootstrapping process 214 may then determine, at 732, whether the hubcandidate is splittable; that is, whether the character string can besplit into separate hub candidates. By way of example, a match from ahub candidate to a known concept may not be found at 716 for a varietyof reasons, the simplest of which may be that the hub candidate is toogeneric to identify a known concept with confidence, is drasticallymisspelled or entered wrong, or, of particular interest, is considered a“higher order” concept that, generally, involves a phrase and even twoor more concepts. By way of example, a user may have typed a declarationthat, even after pre-processing at 706, results in the higher order hubcandidate character string “all movies with Johnny Depp or EdwardNorton.” To facilitate this process, bootstrapping process 214 may keepa list of “connector words” such as “and” or “or,” among others. Inparticular embodiments, if a confident match for a character string suchas this can't be found, bootstrapping process 214 may make thedetermination at 732 as to whether the character string corresponding tothe hub candidate is splittable. In particular embodiments, if it isdetermined that the character string is splittable at 732 (e.g.,bootstrapping process 214 identified connector words in the characterstring), then bootstrapping process 214 splits the character string intoone or more hub candidates at 742. For each of these split hubcandidates (e.g., “all movies with Johnny Depp” and “all movies withEdward Norton”), bootstrapping process 214 then proceeds as beforebeginning with step 708.

As another example, bootstrapping process 214 may use a second list of“common phrase language” that it may use to determine if a hub candidateis splittable or, more particularly, reducible at 732. By way ofexample, in the above example, the part of the character string thatreads “all movies with” may be identified as common phrase language andremoved, thereby resulting in the separate hub candidates of simply“Johnny Depp” and “Edward Norton.”

However, in particular embodiments, in cases in which the characterstring is not splittable such as, for example, when bootstrappingprocess 214 is unable to identify any connector words (e.g., and, or,etc.), bootstrapping process 214 reverts to a fallback mechanism inwhich it automatically creates a “fallback node (e.g., a fallback hubnode 304);” that is, bootstrapping process 214 assumes that the usercorrectly entered a concept meaningful to him or her. More particularly,bootstrapping process 214 calls node-generating API 212 at 734, whichthen generates a new hub node 304 in social graph database 206corresponding to the hub candidate character string. In particularembodiments, bootstrapping process may or may not then calledge-generating API 210 at 738 to generate, at 740, an edge from theuser's node 302 to the new fallback hub node 304.

In particular embodiments, the determination of whether or not to createa fallback hub node 304 is based on several criteria, many of which areimplementation specific. As an example, consider that a user may haveentered “The Matrix trilogy.” In particular embodiments, bootstrappingprocess 214 may have the intelligence to determine, based on informationin concept database 216, that this character string actually refers tothree Matrix movies. In particular embodiments, bootstrapping process214 may cause a new hub node 304 for the matrix trilogy to be generated,but more likely, may generate edges to each of the three hub nodes 304corresponding to the original Matrix movie and the two sequels,respectively. In particular embodiments, bootstrapping process 214 mayalso then cause edges to be created between the individual hub nodes 304associated with the Matrix movies.

FIG. 8 shows a flowchart illustrating an example method for validating ahub node 304. In particular embodiments, when a fallback hub node 304 isgenerated because a confident match could not be found, the best matchesthat were found may be stored with the fallback node or with an edgethat connects the fallback node to the user's node 302. In this way, ifand when the user navigates to the hub associated with the fallback hubnode 304 at 802, the user may be presented, at 804, with a list ofsuggestions corresponding to the list of best matches that were belowthe threshold needed to be considered a match at 716. By way of example,the hub corresponding to the fallback hub node 304 may be flagged insocial graph database 206. More particularly, the user may be promptedwith a list of names (e.g., hyperlinks) or thumbnail images of the hubsor profile pages of associated nodes corresponding to the best matchesor, alternately, with an “ad” or “social ad” asking the user if he orshe would like to be redirected to one of the suggested hubs or profilepages of associated nodes corresponding to the best matches. Inparticular embodiments, if the user does not click on a redirect link orotherwise select to be redirected to one of the suggested matches, thenbootstrapping process 214 confirms that the fallback hub node 304 isindeed a valid node at 808. In particular embodiments, if the userclicks on a redirect link or otherwise selects to be redirected to oneof the suggested matches, then bootstrapping process 214 redirects theuser to the hub corresponding to the node associated with the selectedredirection at 810, causes the edge from the user's node to the fallbackhub node 304 to be remapped at 812 to the selected match's node (whichmay involve deleting the old edge to the fallback node and generating anew edge to the selected node). In some embodiments, if it isdetermined, at 814, that the number of users connected to the samefallback hub node 304 but who, after navigating to the hub correspondingto the fallback node, selected the same redirection is greater than athreshold, then bootstrapping process 214 causes the edges connectedwith that fallback hub node 304 to be remapped at 816 to the nodecorresponding to the selected redirection. In one embodiment, in such acase, the fallback hub node 304 is then deleted at 818.

In other alternate embodiments, rather then generating fallback hubnodes 304, bootstrapping process 214 may cause an interface to bepresented to the user upon the user's next login that displays the hubcandidates that bootstrapping process 214 was unable to match to knownconcepts. The interface may prompt the user to select to keep the user'sentered text (corresponding to the unmatched hub candidate) unchanged,which may then result in the creation of a new hub node 304 for the hubcandidate and the creation of a new edge from the user's node to the newhub node 304. Otherwise, the user may be prompted to select from ashortlist of best matches, which may then result in the creation of anedge from the user's node to the node corresponding to the selectedmatch.

In particular embodiments, similar processes can be used to create newhub nodes 304 from existing hub nodes 304 as well as new edges fromexisting hub nodes 304 to other existing or new hub nodes 304.Additionally, in particular embodiments, there may be a review process,which may be machine or human implemented, that reviews newly generatednodes and determines whether to delete them or to add their content(e.g., merge them) with other existing nodes. By way of example, a newlygenerated hub node 304 for the concept “I love Britney Spears” may bedeleted and any content in it may be added to an existing hub node 304for “Britney Spears.” Additionally, if it is determined that a hub node304 and its corresponding hub aren't being accessed by enough users orenough in a given time interval, the node may be deleted. Furthermore,as social network environment 20 may support users who use a variety oflanguages, in particular embodiments, the typeahead processes 204 and208 or bootstrapping process 214 may decide to create a new node relatedto a particular concept identified from a user of one language eventhough the node already exists in another language. Alternately, thetypeahead processes 204 and 208 or bootstrapping process 214 may decideto connect a user that typically uses one language to an existing nodecorresponding to the particular concept identified from the user eventhough the information stored with the existing node is in a differentlanguage.

FIG. 9, referenced above, shows a flowchart illustrating an examplemethod, implemented by or in conjunction with one or more server-siderecommendation-generating processes 218 (hereinafter referred to asrecommendation-generating process 218) illustrated in FIG. 2B, forgenerating one or more recommendations, and particularly recommendedhubs corresponding to respective hubs nodes 304 in social graph database206, for display to a user requesting or currently viewing a particularhub, based at least in part on information extracted from social graphdatabase 206. In particular embodiments, the recommendation-generatingprocess 218 leverages the social graph data from social graph database206, which may include one or more searchable or queryable indexesgenerated by indexing social graph database 206, to generaterecommendations, and particularly recommended hubs, based on at leasttwo contributions from the social graph data.

In particular embodiments, recommendation-generating process 218determines recommended hubs for a particular user (the “requestinguser”) requesting or currently viewing a particular hub (the “requestedhub”), and further causes the recommended hubs to be displayed or listedin recommendations section 510 of the hub described above with referenceto FIG. 5. In particular embodiments, recommendation-generating process218 determines recommended hubs based on leveraging informationextracted from social graph database 206 including information about oneor more of: the requesting user, the requested hub, friends of the userwhose user nodes 302 may or may not also be connected to the requestedhub's hub node 304 with respective edges, and other hubs havingrespective hub nodes 304 that are also connected to the requested hub'shub node 304. As described above, for example, the recommended hubsdisplayed in recommendations section 510 may include hubs that are likedor otherwise connected (with edges in social graph database 206) tofriends of the requesting user (as defined by edges in social graphdatabase 206), and particularly friends that are also connected to therequested hub (with edges in social graph database 206). As anotherexample, the recommended hubs displayed in recommendations section 510may include hubs that users, and particularly friends of the requestinguser (as defined by edges in social graph database 206), also like orare otherwise connected to (with edges in social graph database 206),but who aren't necessarily connected with the requested hub (with edgesin social graph database 206). As another example, the recommended hubsdisplayed in recommendations section 510 may include hubs that areconnected to the requested hub (with edges in social graph database 206)and one or more friends of the requesting user (as defined by edges insocial graph database 206). As another example, the recommended hubsdisplayed in recommendations section 510 may include hubs that areconnected to the requested hub (with edges in social graph database 206)but that aren't necessarily connected with friends of the requestinguser (as defined by edges in social graph database 206). Generally, asdescribed above, one goal or motivation for displaying recommended hubsto the requesting user currently viewing or requesting a particular hubis to provide the user with recommended hubs the user may be interestedin viewing or interacting with and, furthermore, facilitate navigationto such recommended hubs from the currently viewed hub and, inparticular embodiments, facilitate the creation of edges connecting theuser to one or more of the recommended hubs the user demonstrates orindicates an interest in.

In particular embodiments, the method begins when social networkenvironment 20 receives a request for a particular hub (the requestedhub) from a particular user (the requesting user) at 902. In particularembodiments, in response to the request, social network environment 20,and particularly page-generating process 200, generates a structureddocument for rendering the hub at the requesting user's client device 30and transmits an initial response that includes the structured documentto the requesting user's client device 30 at 904. The structureddocument transmitted to the requesting user may be a base structureddocument that includes markup language code as well as various codesegments, scripts, resources, or other information or content forserving the requested hub to the client for rendering by the client'sweb browser 202. In some embodiments, the base structured document mayinclude code for rendering one or more portions of the requested hubincluding code for displaying portions of the recommendations section510 but may not include the recommended hubs themselves in the form ofhub names or other identifiers 512 and images 514; that is, in oneimplementation, social network environment 20 may transmit thestructured document at 904 in the initial response before therecommended hubs are determined by recommendation-generating process218. In this way, the client's web browser 202 may start rendering thestructured document and downloading resources for rendering therequested hub as recommendation-generating process 218 completes it'sdetermination of recommended hubs.

In such embodiments, in parallel with or after the generation of thebase structured document or sending of the initial response,recommendation-generating process 218 generates recommended hubs thatare then transmitted in one or more subsequent responses to therequesting user's client device 30 at 922 for rendering by the client'sweb browser 202. In one particular embodiment, after the request for thehub is received by social network environment 20 at 902, page-generatingprocess 200 or other process executing within social network environment20 transmits an instruction or query at 906 to recommendation-generatingprocess 218 requesting one or more recommended hubs for display inrecommendations section 510. In an example embodiment, the instructionor query includes information such as an identifier of the user (e.g., auser ID that identifies the requesting user's user node 302) and anidentifier of the requested hub (e.g., a hub ID that identifies therequested hub's hub node 304).

In particular embodiments, in response to the instruction or query at906, recommendation-generating process 218 then determines or identifiesa first data set, at 908, that includes hubs that are each connected(via edges in social graph database 206) with one or more users who are,in turn, each connected with both the requesting user (e.g., friends ofthe requesting user) and also connected with the requested hub (e.g.,users that also like the requested hub). In particular embodiments, inparallel with or after determining the first data set at 908,recommendation-generating process 218 determines a second data set, at910, that includes hubs that are each connected both with the requestedhub (via edges in social graph database 206) and also connected with oneor more users who, in turn, are connected (via edges in social graphdatabase 206) to the requesting user (e.g., friends of the requestinguser). Generally, the first and second data sets may include one or moreof the same hubs.

In one particular embodiment, as described above, social graph database206 may include one or more queryable (searchable) indexes generated byindexing the data within social graph database 206 (alternately, inanother embodiment, the indexes may be stored in one or more data storesor databases outside of social graph database 206). In one particularimplementation, an indexing process 220 generates or updates the indexesperiodically (e.g., daily). Additionally, or alternately, the indexesmay be updated dynamically in response to the creation of new nodes ornew edges in social graph database 206 as well as in response to otheractions (e.g., in response to interactions between users and hubs or inresponse to edits made to user profile pages or hubs). In one particularimplementation, indexing process 220 may generate a plurality of indexesto facilitate the determinations at 908 and 910. By way of example,indexing process 220 may generate and maintain an index of allregistered users that is indexed by user ID (e.g., the identifiers ofthe users and respective user nodes 302 in social graph database 206)and which includes, for each user ID, the set of other users identifiedby their respective user IDs whose respective user nodes 302 areconnected to the user node 302 corresponding to the particular user IDin the index. As another example, indexing process 220 may generate andmaintain another index of all registered users that is again indexed byuser ID but which includes, for each user ID, the set of hubs orrespective hub nodes 304 identified by their respective hub IDs (e.g.the identifiers of the hubs and respective hub nodes 304 in social graphdatabase 206) that are connected to the user node 302 corresponding tothe particular user ID in the index. As another example, indexingprocess 220 may generate and maintain another index of all the hubsindexed by hub ID and that includes, for each hub ID, the set of otherhubs and respective hub nodes 304 identified by hub ID that areconnected to the hub node 304 corresponding to the particular hub ID inthe index. As another example, indexing process 220 may generate andmaintain another index of all the hubs indexed by hub ID and thatincludes, for each hub ID, the set of users and respective user nodes302 identified by user ID that are connected to the hub node 304corresponding to the particular hub ID in the index.

In such embodiments, recommendation-generating process 218 may determinethe first and second data sets at 908 and 910, respectively, by queryingthe indexes generated by indexing process 220. This may involve queryingindexing process 220 itself or some other process that is configured toreceive queries and return results based on searching one or more of theindexes. By way of example, in a particular implementation,recommendation-generating process 218 determines the first data set at908 by sending a first nested query to indexing process 220 that, in afirst part or step of the first nested query, instructs indexing processto identify all the user IDs corresponding to users who are connected(e.g., friends) with the requesting user. In a second part of the nestedquery, indexing process 220 is instructed to identify which of the userIDs identified in the first part correspond to users who are alsoconnected with the requested hub. In a third part of the first nestedquery, indexing process 220 is instructed to return, torecommendation-generating process 218, the hub IDs corresponding to thehubs connected with the user IDs identified in the second part of thequery (but in particular embodiments, excluding those hub IDscorresponding to hubs already connected to the requesting user) as wellas the user IDs themselves matched with each of the hub IDs. In asimilar fashion, recommendation-generating process 218 may determine thesecond data set at 910 by sending a second nested query to indexingprocess 220 that instructs indexing process to determine all the hub IDscorresponding to hubs that are connected with the requested hub and thenreturn, to recommendation-generating process 218, the hub IDscorresponding to the ones of the identified hubs that are also connectedwith one or more users connected with the requesting user (but inparticular embodiments, excluding those hub IDs corresponding to hubsalready connected to the requesting user) as well as the user IDsthemselves matched with each of the hub IDs.

Additionally, in some embodiments, the hub indexes generated by indexingprocess 220 are indexed, arranged, or otherwise searchable by hubcategory (e.g., movie, music, activity, sport, etc.). In someembodiments, the hubs returned in the first and second data sets mayonly include hubs sharing the same category as the requested hub. Inother embodiments, hubs of different categories may be included in thereturned data sets.

Thus, in various example embodiments, each of the first and second datasets includes a list or index of hubs (e.g., identified by hub ID) aswell as, for each hub, a set of one or more users (e.g., identified byuser ID) connected with the respective hub. In one implementation,recommendation-generating process 218 then generates a score for eachhub identified in the first and second data sets at 912 and subsequentlyranks the hubs based on their respective scores at 914 to generate asingle combined or correlated list of ranked hubs that are candidates(hereinafter also referred to as “candidate hubs”) for recommended hubs.In particular embodiments, recommendation-generating process 218 scoreseach hub in each of the first and second data sets based at least inpart on the number of users (i.e., the friends of the requesting user)returned with the respective hub in the first or second data sets.

In one embodiment, recommendation-generating process 218 scores the hubsin each of the first and second data sets and then combines theresulting scores. By way of example, if a hub in the first set isconnected with five of the requesting user's friends, that hub may beassigned a score or weight of five by recommendation-generating process218. Similarly, if a hub in the second set is connected with four of therequesting user's friends, that hub may be assigned a score or weight offour by recommendation-generating process 218. In such animplementation, recommendation-generating process may then generate acombined data set that includes all of the hubs in each of the first andsecond data sets and combine or correlate the scoring results based oneach of the first and second data sets to generate a single correlatedscore for each of the hubs at 912 in the combined data set. The singlecorrelated scores for the respective candidate hubs are then used inranking the hubs in the correlated ranked list of hubs at 914. In oneimplementation, the weights assigned to the hubs in the first and seconddata sets are themselves weighted equally by recommendation-generatingprocess 218. By way of example, a hub with a weight of five from thefirst data set may be assigned a score of five in the single correlatedranked list. Similarly, a hub with a weight of four from the second dataset may be assigned a score of four in the single correlated rankedlist. Additionally, as the first and second data sets may share commonhubs, if a hub returned in both of the first and second data sets wasassigned a weight of six based on the first data set and assigned aweight of three based on the second data set, recommendation-generatingprocess may sum the individual weights and assign the hub a score ofnine in the single correlated ranked list (6+3=9). However, as thefriends used to generate the weights may be shared in the first andsecond data sets, recommendation-generating process 218 may reduce thecombined score to account for this. By way of example, continuing theabove example, assuming that a hub found in both data sets also sharestwo friends in each data set, the resultant correlated score for the hubmay be calculated as seven (6+3−2=7).

However, in particular embodiments, recommendation-generating process218 may first combine the first and second data sets to generate onecombined data set and then score each of the hubs in the resultantcombined data set. In this way, hubs and associated users shared betweenthe data sets may be accounted for, if desired, before generating ascore for the hub.

In particular embodiments, recommendation-generating process 218determines a score for each hub in the combined data set at 912 based onother factors other than simply the number of the requesting user'sfriends connected with the respective hub. By way of example, in oneembodiment, recommendation-generating process 218 may query indexingprocess 220 for one or more other data sets. As an example, in anadditional or alternate embodiment, recommendation-generating process218 may determine a third data set that includes all the hubs connectedwith the requested hub, or all the hubs that are connected with therequested hub and not connected to any of the requesting user's friends.As another example, recommendation-generating process 218 may determinea fourth data set that includes all of the hubs connected with therequesting user's friends, or all of the hubs connected with therequesting user's friends but not connected to the requested hub. Thesethird or fourth data sets may be used in augmenting the data in thefirst and second data sets or to provide additional criteria (e.g., aglobal filter that indicates an overall popularity of each of the hubs)with which to score the hubs in the first and second data sets.Furthermore, the third and fourth data sets may be particularly usefulin cases in which there are not any friends of the user that areconnected with the requested hub or in which none of the hubs connectedwith the requested hub are connected to any of the requesting user'sfriends.

In one example implementation, recommendation-generating process 218 mayweight the hubs in such third or fourth data sets differently than thehubs in the first and second data sets. By way of example, in oneimplementation, each hub in the third or fourth data sets are weightedaccording to the total number of user nodes 302 connected with therespective hub's hub node 304. However, when combining the first,second, third, and fourth data sets and calculating a correlated scorefor each hub in the combined data set at 912, the number of total usersconnected with each hub in the third and fourth data sets may accountfor a smaller contribution to the single correlated score. By way ofexample, if “hub A” is found in each of the first, second, third, andfourth data sets and is associated with 5 users in the first data set, 4non-shared users in the second data set, 50 users in the third data set,and 75 users in the fourth data set, recommendation-generating processmay calculate the correlated score for the hub as 5a+4b+50c+75d where a,b, c, and d are the weights with which the respective number of usersare multiplied by. As an example, in one implementation, a=1, b=1,c=0.01, and d=0.01 such that the correlated score for the hub A is5+4+0.5+0.75=10.25.

Additionally or alternately, in some embodiments,recommendation-generating process 218 may determine, at 912, the singlecorrelated score for each hub in the first and second data sets (in someembodiments the hubs in the third and fourth data sets that are not alsoin one or more of the first and second data sets are not scored) bysumming or otherwise combining, for each hub in the combined data set, anumber of coefficient scores that are, in turn, generated for each hubin the combined data set and each user connected with the respectivehub. In particular embodiments, a coefficient score in this sense refersto the strength of the connection (as defined or represented by an edge)or plurality of connections. The coefficient scores generated byrecommendation-generating process 218 for a particular hub andassociated users may be based on numerous and various criteria. By wayof example, determining a single correlated score for each hub in thecombined data set at 912 may involve recommendation-generating process218 generating one or more coefficient scores between each hub in thecombined data set and each of the requesting user's friends connected tothe respective hub, one or more coefficient scores between each hub inthe combined data set and the requesting user, one or more coefficientscores between each hub in the combined data set and the requested hub,or one or more coefficient scores between the requesting user and eachof the users connected with a hub from the combined data set.Determining a single correlated score for each hub in the combined dataset at 912 may additionally involve summing or otherwise combining eachof the coefficient scores to generate the single correlated score foreach hub.

The coefficient scores generated for each hub or user connected with thehub may be based on factors such as, by way of example and not by way oflimitation, the relationship of the user to the hub; the relationship ofthe user to the requesting user (e.g., friend, relative, spouse, etc.);a level or frequency of interaction between the hub and the user (e.g.,how many times the user views the hub or how much content was added tothe hub by the user over a period of time); a level or frequency ofinteraction between the user and the requesting user (e.g., how timesthe users viewed one another's profile pages or how many times the usersposted comments, wall (feed) postings, sent messages, or otherwiseinteracted with one another's profile pages over a period of time); thenumber of user nodes 302, hub nodes 304, or total nodes connected toboth the hub and the user, or connected to both the user and therequesting user, or connected to both the hub and the requested hub, orconnected to both the hub and the requested user; the quantity orquality of shared content between the hub and the requested hub, etc.

In a particular implementation, in order to calculate coefficient scoresand subsequently single correlated scores for each of the hubs in thecombined data set, the data results returned in the first and seconddata sets (and in some embodiments the third and fourth data sets aswell) are first sent by recommendation-generating process (or directlyfrom indexing process 220) to a data mining system such as, for example,HIVE (a data warehouse infrastructure built on top of HADOOP) whereHADOOP then runs or executes a number of MapReduce jobs or processes onthe data to generate the coefficient scores which are then used byrecommendation-generating process 218 to generate the single correlatedscores for each hub at 912.

In particular embodiments, recommendation-generating process 218 ranksthe hubs by their respective correlated scores and generates a rankedlist of hubs (e.g., a ranked list of hub IDs) at 914 with the hubshaving the highest correlated scores representing the most relevanthubs. In particular embodiments, recommendation-generating process 218then selects, at 916, the top x (e.g., four in the illustratedembodiment) hubs having the highest correlated scores as the recommendedhubs to be displayed in recommendations section 510.

In particular embodiments, recommendation-generating process 218 thencommunicates, at 918, the hub IDs of the recommended hubs topage-generating process 200 or other process that then generates, at920, code including, for example, HTML or other markup language code aswell as, in some embodiments, various other code segments or resourcesincluding, for example, image resources for use in rendering therecommended hub names 512 or images 514 in recommendations section 510,and in some embodiments, code segments for implementing hyperlinks thatdirect the user to a recommended hub upon clicking or otherwiseselecting a recommended hub name text field 512 or hub image 514. Thecode and resources are then sent to the user's client device 30 in asubsequent response at 922 for rendering by the client's web browser202. In particular embodiments, the subsequent response sent at 922 issent using AJAX or other asynchronous techniques as the base structureddocument for rendering the requested hub may have already been sent inan initial response at 904. In an alternate embodiment, social networkenvironment 20 waits for recommendation-generating process 218 toprovide the hub IDs of the recommended hubs and includes the code orresources for displaying the recommended hub names 512 or hub images 514with the rest of the structured document for rendering the hub at theclient device 30 prior to sending the structured document to therequesting user.

In some embodiments, code for rendering user names (e.g., text) 516 oruser images 518 (e.g., user profile pictures or avatars) of a selectsubset of the users connected with each of the recommended hubs and therequesting user is also sent with the subsequent response at 922 forrendering and display next to the respective recommended hub inrecommendations section 510. Additionally or alternately, the subsequentresponse sent at 922 may include code for displaying a text string foreach of the recommended hubs in proximity to the respective recommendedhub that reads, for example, “n of your friends also like this,” where nis the number of the requesting user's friends that are connected to therespective recommended hub or “these friends also like this,” where“these friends” are represented by the user names 516 or user images 518displayed next to the respective recommended hub, or “n of your friendsalso like this including:”.

Furthermore, in particular embodiments, upon clicking or otherwiseselecting a hyperlink (e.g., clicking text 512 or images 514)corresponding to a particular one of the recommended hubs, the user maybe prompted with a user interface that asks the user if he or she wouldlike to be connected with the recommended hub. By way of example, uponclicking an image 514 corresponding to a recommended hub, the user's webbrowser 202 may send a request for the hub to social network environment20. In response to the request, social network environment 20, andparticularly, page-generating process 200 constructs a structureddocument to be sent to the web browser 202 for rendering. Prior to, inparallel with, or after the construction or sending of the structureddocument corresponding to the selected recommended hub, social networkenvironment 20, may send a response to the web browser 202 that causesthe web browser to display the user interface asking the user if he orshe would like to be connected with the recommended hub (e.g., the userinterface may present a link that reads “I like hub A,” where hub A isthe name of the selected recommended hub. Alternately, the initialresponse or subsequent response that included the recommended hubs mayinclude one or more code segments (e.g., JavaScript) that when executedby the web browser 202 implement a client-side process that recognizeswhen recommended hubs are selected and which may cause the userinterface to be displayed and which may further use asynchronoustechniques (e.g., AJAX) or other suitable techniques to communicate amessage to social network environment 20 that a recommended hub wasselected. In response to the user indicating a desire to be connectedwith the selected recommended hub (e.g., by clicking the link in theuser interface prompt), the client-side process may use asynchronoustechniques (e.g., AJAX) or other suitable techniques to communicate amessage to social network environment 20 that the user desired to beconnected with the selected recommended hub. In response,edge-generating API 210 to cause an edge to be created in social graphdatabase 206 that connects the user to the selected recommended hub.

In particular embodiments, as described above, wall (or newsfeed/activities feed) section 501 a, or other feed or activities sectionof the hub, displays comments, status updates, wall posts and other useractivities associated with the user and friends of the user that areviewing the hub. The wall (or news feed/activities feed) section 501 a,or other feed or activities section of the hub may also displaycomments, status updates, wall posts and other user activities and usergenerated content that are related to the concept for which the hub wascreated as well as, in some embodiments, the concepts associated withthe recommended hubs determined for the currently requested or viewedhub. More particularly, recommendation-generating process 218 mayperform a search on comments, status updates, wall posts and otheruser-generated content and user activities associated with therequesting user and friends of the requesting user filtered by concept;that is, a keyword search for keywords related to the concept of thecurrently requested or viewed hub (and potentially keywords related tothe concepts associated with the recommended hubs) in these streams ofuser feeds or activities related to the requesting user and therequesting user's friends, and display this subset of user content oractivities in the wall or feed section of the currently requested orviewed hub.

Moreover, those of skill in art will readily be able to apply theteachings described with reference to the flowchart of FIG. 9 todetermine and cause to be displayed recommended hubs (or recommendedusers) to a user requesting or currently viewing another user's profilepage within a recommendations section displayed on the other user'sprofile page.

The applications or processes described herein can be implemented as aseries of computer-readable instructions, embodied or encoded on orwithin a tangible data storage medium, that when executed are operableto cause one or more processors to implement the operations describedabove. While the foregoing processes and mechanisms can be implementedby a wide variety of physical systems and in a wide variety of networkand computing environments, the computing systems described belowprovide example computing system architectures of the server and clientsystems described above, for didactic, rather than limiting, purposes.

FIG. 10 illustrates an example computing system architecture, which maybe used to implement a server 22 a, 22 b. In one embodiment, hardwaresystem 1000 comprises a processor 1002, a cache memory 1004, and one ormore executable modules and drivers, stored on a tangible computerreadable medium, directed to the functions described herein.Additionally, hardware system 1000 includes a high performanceinput/output (I/O) bus 1006 and a standard I/O bus 1008. A host bridge1010 couples processor 1002 to high performance I/O bus 1006, whereasI/O bus bridge 1012 couples the two buses 1006 and 1008 to each other. Asystem memory 1014 and one or more network/communication interfaces 1016couple to bus 1006. Hardware system 1000 may further include videomemory (not shown) and a display device coupled to the video memory.Mass storage 1018, and I/O ports 1020 couple to bus 1008. Hardwaresystem 1000 may optionally include a keyboard and pointing device, and adisplay device (not shown) coupled to bus 1008. Collectively, theseelements are intended to represent a broad category of computer hardwaresystems, including but not limited to general purpose computer systemsbased on the x86-compatible processors manufactured by Intel Corporationof Santa Clara, Calif., and the x86-compatible processors manufacturedby Advanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well asany other suitable processor.

The elements of hardware system 1000 are described in greater detailbelow. In particular, network interface 1016 provides communicationbetween hardware system 1000 and any of a wide range of networks, suchas an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Massstorage 1018 provides permanent storage for the data and programminginstructions to perform the above-described functions implemented in theservers 22 a, 22 b, whereas system memory 1014 (e.g., DRAM) providestemporary storage for the data and programming instructions whenexecuted by processor 1002. I/O ports 620 are one or more serial and/orparallel communication ports that provide communication betweenadditional peripheral devices, which may be coupled to hardware system1000.

Hardware system 1000 may include a variety of system architectures; andvarious components of hardware system 1000 may be rearranged. Forexample, cache 1004 may be on-chip with processor 1002. Alternatively,cache 1004 and processor 1002 may be packed together as a “processormodule,” with processor 1002 being referred to as the “processor core.”Furthermore, certain embodiments of the present invention may notrequire nor include all of the above components. For example, theperipheral devices shown coupled to standard I/O bus 1008 may couple tohigh performance I/O bus 1006. In addition, in some embodiments, only asingle bus may exist, with the components of hardware system 1000 beingcoupled to the single bus. Furthermore, hardware system 1000 may includeadditional components, such as additional processors, storage devices,or memories.

In one implementation, the operations of the embodiments describedherein are implemented as a series of executable modules run by hardwaresystem 1000, individually or collectively in a distributed computingenvironment. In a particular embodiment, a set of software modulesand/or drivers implements a network communications protocol stack,browsing and other computing functions, optimization processes, and thelike. The foregoing functional modules may be realized by hardware,executable modules stored on a computer readable medium, or acombination of both. For example, the functional modules may comprise aplurality or series of instructions to be executed by a processor in ahardware system, such as processor 1002. Initially, the series ofinstructions may be stored on a storage device, such as mass storage1018. However, the series of instructions can be tangibly stored on anysuitable storage medium, such as a diskette, CD-ROM, ROM, EEPROM, etc.Furthermore, the series of instructions need not be stored locally, andcould be received from a remote storage device, such as a server on anetwork, via network/communications interface 1016. The instructions arecopied from the storage device, such as mass storage 1018, into memory1014 and then accessed and executed by processor 1002.

An operating system manages and controls the operation of hardwaresystem 1000, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Of course, other implementations are possible.For example, the nickname generating functions described herein may beimplemented in firmware or on an application specific integratedcircuit.

Furthermore, the above-described elements and operations can becomprised of instructions that are stored on storage media. Theinstructions can be retrieved and executed by a processing system. Someexamples of instructions are software, program code, and firmware. Someexamples of storage media are memory devices, tape, disks, integratedcircuits, and servers. The instructions are operational when executed bythe processing system to direct the processing system to operate inaccord with the invention. The term “processing system” refers to asingle processing device or a group of inter-operational processingdevices. Some examples of processing devices are integrated circuits andlogic circuitry. Those skilled in the art are familiar withinstructions, computers, and storage media.

The present disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsherein that a person having ordinary skill in the art would comprehend.Similarly, where appropriate, the appended claims encompass all changes,substitutions, variations, alterations, and modifications to the exampleembodiments herein that a person having ordinary skill in the art wouldcomprehend. By way of example, while embodiments of the presentinvention have been described as operating in connection with a socialnetworking website, the present invention can be used in connection withany communications facility that supports web applications. Furthermore,in some embodiments the term “web service” and “web-site” may be usedinterchangeably and additionally may refer to a custom or generalizedAPI on a device, such as a mobile device (e.g., cellular phone, smartphone, personal GPS, personal digital assistance, personal gamingdevice, etc.), that makes API calls directly to a server.

1. A method comprising: maintaining, by one or more computing systems,access to a data store of information corresponding to one or more of aplurality of users of a social network environment and one or more of aplurality of concepts and comprising: a plurality of nodes including aset of user nodes that each correspond to a respective user, and a setof concept nodes that each correspond to a respective concept, each nodeof the sets of nodes being associated with a corresponding structureddocument; and a plurality of edges that each define a connection betweena corresponding pair of nodes from the plurality of nodes; for each ofone or more users: scanning, by one or more of the computing systems,one or more items of content stored or identified with the user nodecorresponding to the user; identifying, by one or more of the computingsystems, a candidate item of content from the one or more items ofcontent; searching, by one or more of the computing systems, the storedinformation for one or more matches between the candidate item ofcontent and existing nodes from the set of concept nodes; determining,by one or more of the computing systems, whether or not a match betweenthe candidate item of content and an existing node from the set ofconcept nodes exists; and when the one or more computing systemsdetermine that at least one match exists: generating, by one or more ofthe computing systems, an edge from the node corresponding to the userto the node from the set of concept nodes for which the best match isdetermined; and when the one or more computing systems determine that nomatch between the candidate item of content and an existing node fromthe set of concept nodes exists: generating, by one or more of thecomputing systems, a new node in the set of concept nodes based on thecandidate item of content; and generating, by the one or more computingsystems, an edge from the node corresponding to the user to the newnode.
 2. The method of claim 1, wherein one or more of the items ofcontent stored or identified with the user node corresponding to theuser each comprises one or more strings of textual characters.
 3. Themethod of claim 2, wherein one or more of the items of content stored oridentified with the user node corresponding to the user each comprisesone or more strings of textual characters stored with the user node fordisplay in a user profile page of the user hosted, at least in part, bythe social network environment.
 4. The method of claim 3, wherein theone or more strings of textual characters were entered by the user inone or more respective user input forms hosted, at least in part, by thesocial network environment for display in the user profile page of theuser.
 5. The method of claim 4, further comprising performing, by theone or more computer systems, preprocessing of the one or more stringsof textual characters.
 6. The method of claim 5, wherein preprocessingof the one or more strings of textual characters comprises applying aset of heuristic rules to parse, separate, or delimit the one or morestrings into one or more separate words or phrases each to be consideredand identified as a candidate item of content.
 7. The method of claim 5,wherein searching the stored information for one or more matches betweenthe candidate item of content and existing nodes from the set of conceptnodes comprises using one or more string matching algorithms to attemptto match each candidate item of content with a string of charactersassociated with each of the existing nodes.
 8. The method of claim 7,wherein determining whether or not a match between the candidate item ofcontent and an existing node from the set of concept nodes existscomprises computing a confidence score for each attempted match based onone or more of: a determination of how well the text in the string oftextual characters of the candidate item of content match the text inthe string of characters associated with the existing node.
 9. Themethod of claim 1, wherein: the data store further comprises conceptinformation comprising a plurality of indexed data objects each of whichcorrespond to a known concept; and searching the stored information forone or more matches between the candidate item of content and existingnodes from the set of concept nodes and determining whether or not amatch between the candidate item of content and an existing node fromthe set of concept nodes exists comprises: searching, by one or more ofthe computing systems, the concept information for one or more matchesbetween the candidate item of content and known concepts; determining,by one or more of the computing systems, whether or not a match betweenthe candidate item of content and a known concept exists; and when theone or more computing systems determine that at least one match betweenthe candidate item of content and known concept exists: determining, byone or more of the computing systems, a best match of the at least onematches; determining, by one or more computing systems, whether or notan existing node exists in set of concept nodes that corresponds to thebest match; and when the one or more computing systems determine that anexisting node exists in the set of concept nodes that corresponds to thebest match; generating, by one or more of the computing systems, an edgefrom the node corresponding to the user to the node from the set ofconcept nodes for which the best match is determined; and when the oneor more computing systems determine that no existing node exists in theset of concept nodes that corresponds to the best match; generating, byone or more of the computing systems, a new node in the set of conceptnodes that corresponds to the best match; and generating, by one or moreof the computing systems, an edge from the node corresponding to theuser to the existing node corresponding to the best match; and when theone or more computing systems determine that a match between thecandidate item of content and known concept does not exist: generating,by one or more of the computing systems, a new node in the set ofconcept nodes that corresponds to the candidate item of content; andgenerating, by one or more of the computing systems, an edge from thenode corresponding to the user to the new node that corresponds to thecandidate item of content.
 10. The method of claim 1, wherein: when theone or more computing systems determine that no match between thecandidate item of content and an existing node from the set of conceptnodes exists and the one or more of the computing systems generate a newnode in the set of concept nodes based on the candidate item of content,the method further comprises: displaying a user interface to the user inresponse to the user requesting a structured document corresponding tothe new concept node, wherein the user interface includes a list ofsuggested matches corresponding to other existing concept nodes otherthan the new concept node; and generating, by the one or more computingsystems, an edge from the node corresponding to the user to the new nodecomprises generating an edge from the node corresponding to the user tothe new node when the user does not select one of the suggested matches.11. A system comprising: one or more processors; and logic encoded inone or more computer-readable tangible storage media that, when executedby the one or more processors, is operable to: maintain access to a datastore of information corresponding to one or more of a plurality ofusers of a social network environment and one or more of a plurality ofconcepts and comprising: a plurality of nodes including a set of usernodes that each correspond to a respective user, and a set of conceptnodes that each correspond to a respective concept, each node of thesets of nodes being associated with a corresponding structured document;and a plurality of edges that each define a connection between acorresponding pair of nodes from the plurality of nodes; for each of oneor more users: scan one or more items of content stored or identifiedwith the user node corresponding to the user; identify a candidate itemof content from the one or more items of content; search the storedinformation for one or more matches between the candidate item ofcontent and existing nodes from the set of concept nodes; determinewhether or not a match between the candidate item of content and anexisting node from the set of concept nodes exists; and when the one ormore computing systems determine that at least one match exists:generate an edge from the node corresponding to the user to the nodefrom the set of concept nodes for which the best match is determined;and when the one or more computing systems determine that no matchbetween the candidate item of content and an existing node from the setof concept nodes exists: generate a new node in the set of concept nodesbased on the candidate item of content; and generate an edge from thenode corresponding to the user to the new node.
 12. The system of claim11, wherein one or more of the items of content stored or identifiedwith the user node corresponding to the user each comprises one or morestrings of textual characters.
 13. The system of claim 12, wherein oneor more of the items of content stored or identified with the user nodecorresponding to the user each comprises one or more strings of textualcharacters stored with the user node for display in a user profile pageof the user hosted, at least in part, by the social network environment.14. The system of claim 13, wherein the one or more strings of textualcharacters were entered by the user in one or more respective user inputforms hosted, at least in part, by the social network environment fordisplay in the user profile page of the user.
 15. The system of claim14, wherein the logic is further operable when executed to preprocessthe one or more strings of textual characters.
 16. The system of claim15, wherein to preprocess the one or more strings of textual characters,the logic is operable when executed to apply a set of heuristic rules toparse, separate, or delimit the one or more strings into one or moreseparate words or phrases each to be considered and identified as acandidate item of content.
 17. The system of claim 15, wherein to searchthe stored information for one or more matches between the candidateitem of content and existing nodes from the set of concept nodes, thelogic is operable when executed to use one or more string matchingalgorithms to attempt to match each candidate item of content with astring of characters associated with each of the existing nodes.
 18. Thesystem of claim 17, wherein to determine whether or not a match betweenthe candidate item of content and an existing node from the set ofconcept nodes exists, the logic is operable to compute a confidencescore for each attempted match based on one or more of: a determinationof how well the text in the string of textual characters of thecandidate item of content match the text in the string of charactersassociated with the existing node.
 19. The system of claim 11, wherein:the data store further comprises concept information comprising aplurality of indexed data objects each of which correspond to a knownconcept; and the logic operable to search the stored information for oneor more matches between the candidate item of content and existing nodesfrom the set of concept nodes and determine whether or not a matchbetween the candidate item of content and an existing node from the setof concept nodes exists comprises logic operable when executed to:search the concept information for one or more matches between thecandidate item of content and known concepts; determine whether or not amatch between the candidate item of content and a known concept exists;and when it is determined that at least one match between the candidateitem of content and known concept exists: determine a best match of theat least one matches; determine whether or not an existing node existsin set of concept nodes that corresponds to the best match; and when itis determined that an existing node exists in the set of concept nodesthat corresponds to the best match; generate an edge from the nodecorresponding to the user to the node from the set of concept nodes forwhich the best match is determined; and when it is determined that noexisting node exists in the set of concept nodes that corresponds to thebest match; generate a new node in the set of concept nodes thatcorresponds to the best match; and generate an edge from the nodecorresponding to the user to the existing node corresponding to the bestmatch; and when it is determined that a match between the candidate itemof content and known concept does not exist: generate a new node in theset of concept nodes that corresponds to the candidate item of content;and generate an edge from the node corresponding to the user to the newnode that corresponds to the candidate item of content.
 20. The systemof claim 11, wherein: when it is determined that no match between thecandidate item of content and an existing node from the set of conceptnodes exists and a new node is generated in the set of concept nodesbased on the candidate item of content, the logic is further operablewhen executed to: display a user interface to the user in response tothe user requesting a structured document corresponding to the newconcept node, wherein the user interface includes a list of suggestedmatches corresponding to other existing concept nodes other than the newconcept node; and the logic is operable when executed to generate anedge from the node corresponding to the user to the new node when theuser does not select one of the suggested matches.